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Ricardo Montañana Gómez
2021-04-11 02:24:03 +02:00
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@@ -133,7 +133,6 @@ dmypy.json
.pre-commit-config.yaml
experimentation/.myconfig
experimentation/.tunnel
results
report_score.sql
datasets_types
table.tex

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results/antiguos/adaBoost.txt Executable file
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******************************************************************************************************
* Best Hyperparameters found for datasets -- adaBoost classifier -- *
******************************************************************************************************
Date Time Type Classifier Dataset Nor Std Accuracy Reference
========== ======== ========== ========== ============================== === === ========= ===========
2020-11-16 13:22:39 gridsearch adaBoost balance-scale 1 1 0.9344000 0.9046280 +
2020-11-21 23:48:05 gridsearch adaBoost balloons 1 0 0.8833330 0.6625000 +
2020-12-10 20:44:44 gridsearch adaBoost breast-cancer-wisc-diag 1 0 0.9754390 0.9743450 +
2020-11-21 23:51:46 gridsearch adaBoost breast-cancer-wisc-prog 1 0 0.8283330 0.7993400 +
2020-11-21 23:53:27 gridsearch adaBoost breast-cancer-wisc 1 0 0.9685610 0.9702560 -
2020-11-21 23:55:47 gridsearch adaBoost breast-cancer 1 0 0.7273440 0.7382400 -
2020-11-22 2:21:32 gridsearch adaBoost cardiotocography-10clases 1 0 0.6848680 0.8277610 -
2020-12-11 9:28:08 gridsearch adaBoost cardiotocography-3clases 1 0 0.8504380 0.9201340 -
2020-12-10 20:24:21 gridsearch adaBoost conn-bench-sonar-mines-rocks 1 0 0.6876890 0.8336540 -
2020-12-10 22:08:18 gridsearch adaBoost cylinder-bands 1 0 0.6504280 0.7691410 -
2020-11-21 23:52:12 gridsearch adaBoost dermatology 1 0 0.9727140 0.9732780 -
2020-11-21 23:48:42 gridsearch adaBoost echocardiogram 1 0 0.8544160 0.8485270 +
2020-11-16 13:22:40 gridsearch adaBoost fertility 1 1 0.8800000 0.8840000 -
2020-11-22 0:07:17 gridsearch adaBoost haberman-survival 1 0 0.7581700 0.7392540 +
2020-11-21 23:54:11 gridsearch adaBoost heart-hungarian 1 0 0.8400930 0.8204750 +
2020-11-21 23:48:31 gridsearch adaBoost hepatitis 1 0 0.8774190 0.8232030 +
2020-11-21 23:57:45 gridsearch adaBoost ilpd-indian-liver 1 0 0.7376070 0.7150280 +
2020-12-10 20:25:26 gridsearch adaBoost ionosphere 1 0 0.9430180 0.9442150 -
2020-06-26 18:09:17 gridsearch adaBoost iris 1 0 0.9800000 0.9786560 +
2020-11-21 23:54:15 gridsearch adaBoost led-display 1 0 0.7150000 0.7102000 +
2020-12-10 21:21:15 gridsearch adaBoost libras 1 0 0.7277780 0.8911110 -
2020-12-10 20:28:48 gridsearch adaBoost low-res-spect 1 0 0.8832660 0.9028200 -
2020-11-21 23:49:21 gridsearch adaBoost lymphography 1 0 0.8643680 0.8554050 +
2020-11-22 1:01:46 gridsearch adaBoost mammographic 1 0 0.8283090 0.8274720 +
2020-12-10 20:10:27 gridsearch adaBoost molec-biol-promoter 1 0 0.8112550 0.8182690 -
2020-12-10 22:30:56 gridsearch adaBoost musk-1 1 0 0.8256360 0.8764710 -
2020-11-22 12:34:38 gridsearch adaBoost oocytes_merluccius_nucleus_4d 1 0 0.8346200 0.8399630 -
2020-11-22 0:11:40 gridsearch adaBoost oocytes_merluccius_states_2f 1 0 0.9236490 0.9299630 -
2020-11-22 0:49:21 gridsearch adaBoost oocytes_trisopterus_nucleus_2f 1 0 0.7401250 0.8333330 -
2020-11-22 0:22:51 gridsearch adaBoost oocytes_trisopterus_states_5b 1 0 0.8541340 0.9315790 -
2020-11-21 23:53:57 gridsearch adaBoost parkinsons 1 0 0.8564100 0.9202210 -
2020-11-22 0:24:41 gridsearch adaBoost pima 1 0 0.7747390 0.7671880 +
2020-11-21 23:55:43 gridsearch adaBoost pittsburg-bridges-MATERIAL 1 0 0.8675320 0.8642860 +
2020-11-21 23:55:40 gridsearch adaBoost pittsburg-bridges-REL-L 1 0 0.6976190 0.6959290 +
2020-11-21 23:56:48 gridsearch adaBoost pittsburg-bridges-SPAN 1 0 0.7321640 0.6891300 +
2020-11-22 0:00:29 gridsearch adaBoost pittsburg-bridges-T-OR-D 1 0 0.8919050 0.8743700 +
2020-11-21 23:58:28 gridsearch adaBoost planning 1 0 0.7256760 0.7255790 +
2020-11-21 23:57:36 gridsearch adaBoost post-operative 1 0 0.7222220 0.7117420 +
2020-11-21 23:57:45 gridsearch adaBoost seeds 1 0 0.9428570 0.9563030 -
2020-11-22 0:26:38 gridsearch adaBoost statlog-australian-credit 1 0 0.6869570 0.6782810 +
2020-11-22 0:35:42 gridsearch adaBoost statlog-german-credit 1 0 0.7690000 0.7562000 +
2020-11-22 0:03:22 gridsearch adaBoost statlog-heart 1 0 0.8407410 0.8422990 -
2020-11-22 0:29:34 gridsearch adaBoost statlog-image 1 0 0.9593070 0.9761940 -
2020-11-22 1:03:33 gridsearch adaBoost statlog-vehicle 1 0 0.7860700 0.8006730 -
2020-12-10 20:32:31 gridsearch adaBoost synthetic-control 1 0 0.9816670 0.9903330 -
2020-11-22 0:29:36 gridsearch adaBoost tic-tac-toe 1 0 0.9853400 0.9853850 -
2020-11-22 0:13:09 gridsearch adaBoost vertebral-column-2clases 1 0 0.8387100 0.8491530 -
2020-11-22 0:08:20 gridsearch adaBoost wine 1 0 0.9888890 0.9932810 -
2020-11-22 0:08:46 gridsearch adaBoost zoo 1 0 0.9600000 0.9603850 -
we have better results 22 times
we have worse results 27 times
we have equal results 0 times
stree used 0 times
bagging used 0 times
adaBoost used 49 times
odte used 0 times

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results/antiguos/analysis.txt Executable file
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****************************************************************************************
* Best model results *
****************************************************************************************
Dataset Reference stree odte adaBoost bagging
============================== ========= =========== =========== =========== ===========
balance-scale 0.904628 0.9488 + 0.9712 + 0.9344 + 0.8352 -
balloons 0.6625 0.866667 + 0.866667 + 0.883333 + 0.683333 +
breast-cancer-wisc-diag 0.974345 0.978932 + 0.982456 + 0.975439 + 0.980702 +
breast-cancer-wisc-prog 0.79934 0.828462 + 0.848718 + 0.828333 + 0.833718 +
breast-cancer-wisc 0.970256 0.965694 - 0.974265 + 0.968561 - 0.977122 +
breast-cancer 0.73824 0.730853 - 0.744949 + 0.727344 - 0.751906 +
cardiotocography-10clases 0.827761 0.666522 - 0.751651 - 0.684868 - 0.702258 -
cardiotocography-3clases 0.920134 0.848074 - 0.861255 - 0.850438 - 0.849022 -
conn-bench-sonar-mines-rocks 0.833654 0.688269 - 0.755052 - 0.687689 - 0.73043 -
cylinder-bands 0.769141 0.632667 - 0.66996 - 0.650428 - 0.666134 -
dermatology 0.973278 0.975454 + 0.986412 + 0.972714 - 0.978156 +
echocardiogram 0.848527 0.847293 - 0.869801 + 0.854416 + 0.869801 +
fertility 0.884 0.88 - 0.89 + 0.88 - 0.89 +
haberman-survival 0.739254 0.764675 + 0.774564 + 0.75817 + 0.738604 -
heart-hungarian 0.820475 0.829924 + 0.843717 + 0.840093 + 0.847107 +
hepatitis 0.823203 0.864516 + 0.877419 + 0.877419 + 0.864516 +
ilpd-indian-liver 0.715028 0.742691 + 0.737577 + 0.737607 + 0.730769 +
ionosphere 0.944215 0.948732 + 0.957223 + 0.943018 - 0.960081 +
iris 0.978656 0.98 + 0.993333 + 0.98 + 0.98 +
led-display 0.7102 0.712 + 0.731 + 0.715 + 0.695 -
libras 0.891111 0.702778 - 0.802778 - 0.727778 - 0.819444 -
low-res-spect 0.90282 0.879492 - 0.913402 + 0.883266 - 0.913384 +
lymphography 0.855405 0.864828 + 0.891724 + 0.864368 + 0.891954 +
mammographic 0.827472 0.829372 + 0.842876 + 0.828309 + 0.837689 +
molec-biol-promoter 0.818269 0.810822 - 0.896104 + 0.811255 - 0.942857 +
musk-1 0.876471 0.789912 - 0.833903 - 0.825636 - 0.817039 -
oocytes_merluccius_nucleus_4d 0.839963 0.808221 - 0.843419 + 0.83462 - 0.792573 -
oocytes_merluccius_states_2f 0.929963 0.911903 - 0.929512 - 0.923649 - 0.729928 -
oocytes_trisopterus_nucleus_2f 0.833333 0.747691 - 0.750964 - 0.740125 - 0.727971 -
oocytes_trisopterus_states_5b 0.931579 0.845361 - 0.876136 - 0.854134 - 0.85408 -
parkinsons 0.920221 0.846154 - 0.866667 - 0.85641 - 0.861538 -
pima 0.767188 0.780002 + 0.789093 + 0.774739 + 0.777379 +
pittsburg-bridges-MATERIAL 0.864286 0.886147 + 0.867532 + 0.867532 + 0.877056 +
pittsburg-bridges-REL-L 0.695929 0.67619 - 0.705238 + 0.697619 + 0.69619 +
pittsburg-bridges-SPAN 0.68913 0.677193 - 0.709357 + 0.732164 + 0.687135 -
pittsburg-bridges-T-OR-D 0.87437 0.902381 + 0.902381 + 0.891905 + 0.882857 +
planning 0.725579 0.725525 - 0.747898 + 0.725676 + 0.736787 +
post-operative 0.711742 0.722222 + 0.722222 + 0.722222 + 0.711111 -
seeds 0.956303 0.961905 + 0.961905 + 0.942857 - 0.961905 +
statlog-australian-credit 0.678281 0.67971 + 0.7 + 0.686957 + 0.684058 +
statlog-german-credit 0.7562 0.762 + 0.777 + 0.769 + 0.762 +
statlog-heart 0.842299 0.848148 + 0.866667 + 0.840741 - 0.866667 +
statlog-image 0.976194 0.959307 - 0.962771 - 0.959307 - 0.962771 -
statlog-vehicle 0.800673 0.801413 + 0.817981 + 0.78607 - 0.79315 -
synthetic-control 0.990333 0.971667 - 0.995 + 0.981667 - 0.996667 +
tic-tac-toe 0.985385 0.987435 + 0.987435 + 0.98534 - 0.875758 -
vertebral-column-2clases 0.849153 0.829032 - 0.845161 - 0.83871 - 0.774194 -
wine 0.993281 0.977778 - 0.994444 + 0.988889 - 0.994444 +
zoo 0.960385 0.970476 + 0.98 + 0.96 - 0.97 +
we have better results 113 times
we have worse results 83 times
stree used 49 times better 25 times worse 24 times
odte used 49 times better 37 times worse 12 times
adaBoost used 49 times better 22 times worse 27 times
bagging used 49 times better 29 times worse 20 times

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results/antiguos/bagging.txt Executable file
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******************************************************************************************************
* Best Hyperparameters found for datasets -- bagging classifier -- *
******************************************************************************************************
Date Time Type Classifier Dataset Nor Std Accuracy Reference
========== ======== ========== ========== ============================== === === ========= ===========
2020-12-19 18:36:31 gridsearch bagging balance-scale 1 0 0.8352000 0.9046280 -
2020-11-19 13:41:55 gridsearch bagging balloons 1 1 0.6833330 0.6625000 +
2020-11-20 3:48:59 gridsearch bagging breast-cancer-wisc-diag 1 1 0.9807020 0.9743450 +
2020-11-11 23:10:52 gridsearch bagging breast-cancer-wisc-prog 1 1 0.8337180 0.7993400 +
2020-11-23 12:10:11 gridsearch bagging breast-cancer-wisc 1 0 0.9771220 0.9702560 +
2020-11-19 23:44:41 gridsearch bagging breast-cancer 1 1 0.7519060 0.7382400 +
2020-11-16 13:37:13 gridsearch bagging cardiotocography-10clases 1 1 0.7022580 0.8277610 -
2020-11-16 13:35:23 gridsearch bagging cardiotocography-3clases 1 1 0.8490220 0.9201340 -
2020-12-12 13:27:08 gridsearch bagging conn-bench-sonar-mines-rocks 1 0 0.7304300 0.8336540 -
2020-12-20 1:53:07 gridsearch bagging cylinder-bands 1 0 0.6661340 0.7691410 -
2020-12-20 4:18:35 gridsearch bagging dermatology 1 0 0.9781560 0.9732780 +
2020-11-19 19:42:13 gridsearch bagging echocardiogram 1 1 0.8698010 0.8485270 +
2020-11-19 15:22:26 gridsearch bagging fertility 1 1 0.8900000 0.8840000 +
2020-11-20 7:04:39 gridsearch bagging haberman-survival 1 1 0.7386040 0.7392540 -
2020-11-23 15:28:21 gridsearch bagging heart-hungarian 1 0 0.8471070 0.8204750 +
2020-11-19 17:44:52 gridsearch bagging hepatitis 1 1 0.8645160 0.8232030 +
2020-12-21 7:18:24 gridsearch bagging ilpd-indian-liver 1 0 0.7307690 0.7150280 +
2020-11-20 7:39:16 gridsearch bagging ionosphere 1 1 0.9600810 0.9442150 +
2020-06-26 11:03:03 gridsearch bagging iris 1 0 0.9800000 0.9786560 +
2021-01-13 13:45:23 gridsearch bagging led-display 1 0 0.6950000 0.7102000 -
2021-01-14 0:28:42 gridsearch bagging libras 1 0 0.8194440 0.8911110 -
2020-12-24 16:13:21 gridsearch bagging low-res-spect 1 0 0.9133840 0.9028200 +
2020-11-20 1:24:35 gridsearch bagging lymphography 1 1 0.8919540 0.8554050 +
2020-12-22 11:05:56 gridsearch bagging mammographic 1 0 0.8376890 0.8274720 +
2020-11-20 2:26:50 gridsearch bagging molec-biol-promoter 1 1 0.9428570 0.8182690 +
2020-12-13 10:54:42 gridsearch bagging musk-1 1 0 0.8170390 0.8764710 -
2020-11-16 13:35:59 gridsearch bagging oocytes_merluccius_nucleus_4d 1 1 0.7925730 0.8399630 -
2020-11-16 13:35:27 gridsearch bagging oocytes_merluccius_states_2f 1 1 0.7299280 0.9299630 -
2020-11-16 13:35:46 gridsearch bagging oocytes_trisopterus_nucleus_2f 1 1 0.7279710 0.8333330 -
2020-11-16 13:35:41 gridsearch bagging oocytes_trisopterus_states_5b 1 1 0.8540800 0.9315790 -
2020-11-20 7:53:07 gridsearch bagging parkinsons 1 1 0.8615380 0.9202210 -
2020-12-23 22:40:26 gridsearch bagging pima 1 0 0.7773790 0.7671880 +
2020-11-21 14:57:13 gridsearch bagging pittsburg-bridges-MATERIAL 1 1 0.8770560 0.8642860 +
2020-11-20 12:30:28 gridsearch bagging pittsburg-bridges-REL-L 1 1 0.6961900 0.6959290 +
2020-11-16 13:35:56 gridsearch bagging pittsburg-bridges-SPAN 1 1 0.6871350 0.6891300 -
2020-11-16 13:35:54 gridsearch bagging pittsburg-bridges-T-OR-D 1 1 0.8828570 0.8743700 +
2020-11-22 0:19:48 gridsearch bagging planning 1 1 0.7367870 0.7255790 +
2020-11-16 13:35:58 gridsearch bagging post-operative 1 1 0.7111110 0.7117420 -
2020-11-20 17:28:36 gridsearch bagging seeds 1 1 0.9619050 0.9563030 +
2020-12-22 19:59:23 gridsearch bagging statlog-australian-credit 1 0 0.6840580 0.6782810 +
2020-12-24 0:02:12 gridsearch bagging statlog-german-credit 1 0 0.7620000 0.7562000 +
2020-11-20 22:17:22 gridsearch bagging statlog-heart 1 1 0.8666670 0.8422990 +
2020-11-16 14:04:13 gridsearch bagging statlog-image 1 1 0.9627710 0.9761940 -
2020-11-16 13:37:33 gridsearch bagging statlog-vehicle 1 1 0.7931500 0.8006730 -
2020-12-25 19:30:56 gridsearch bagging synthetic-control 1 0 0.9966670 0.9903330 +
2020-12-22 18:42:57 gridsearch bagging tic-tac-toe 1 0 0.8757580 0.9853850 -
2020-11-16 13:37:03 gridsearch bagging vertebral-column-2clases 1 1 0.7741940 0.8491530 -
2020-11-21 2:52:23 gridsearch bagging wine 1 1 0.9944440 0.9932810 +
2020-11-20 22:21:27 gridsearch bagging zoo 1 1 0.9700000 0.9603850 +
we have better results 29 times
we have worse results 20 times
we have equal results 0 times
stree used 0 times
bagging used 49 times
adaBoost used 0 times
odte used 0 times

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results/antiguos/odte.txt Executable file
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******************************************************************************************************
* Best Hyperparameters found for datasets -- odte classifier -- *
******************************************************************************************************
Date Time Type Classifier Dataset Nor Std Accuracy Reference
========== ======== ========== ========== ============================== === === ========= ===========
2020-12-23 7:47:46 gridsearch odte balance-scale 1 0 0.9712000 0.9046280 +
2021-01-11 11:52:54 gridsearch odte balloons 1 0 0.8666670 0.6625000 +
2020-12-16 22:24:05 gridsearch odte breast-cancer-wisc-diag 1 0 0.9824560 0.9743450 +
2020-12-16 20:10:07 gridsearch odte breast-cancer-wisc-prog 1 0 0.8487180 0.7993400 +
2020-11-22 14:14:12 gridsearch odte breast-cancer-wisc 1 0 0.9742650 0.9702560 +
2020-11-18 23:04:30 gridsearch odte breast-cancer 1 1 0.7449490 0.7382400 +
2020-12-07 23:56:19 gridsearch odte cardiotocography-10clases 1 0 0.7516510 0.8277610 -
2020-12-07 16:13:18 gridsearch odte cardiotocography-3clases 1 0 0.8612550 0.9201340 -
2020-12-21 13:58:11 gridsearch odte conn-bench-sonar-mines-rocks 1 0 0.7550520 0.8336540 -
2020-12-18 5:26:37 gridsearch odte cylinder-bands 1 0 0.6699600 0.7691410 -
2020-12-17 18:10:44 gridsearch odte dermatology 1 0 0.9864120 0.9732780 +
2020-11-22 5:37:43 gridsearch odte echocardiogram 1 0 0.8698010 0.8485270 +
2020-11-26 0:18:42 gridsearch odte fertility 1 0 0.8900000 0.8840000 +
2020-11-22 21:36:33 gridsearch odte haberman-survival 1 0 0.7745640 0.7392540 +
2020-11-22 18:24:35 gridsearch odte heart-hungarian 1 0 0.8437170 0.8204750 +
2020-11-22 4:30:37 gridsearch odte hepatitis 1 0 0.8774190 0.8232030 +
2020-12-22 9:29:28 gridsearch odte ilpd-indian-liver 1 0 0.7375770 0.7150280 +
2020-12-17 3:02:23 gridsearch odte ionosphere 1 0 0.9572230 0.9442150 +
2020-11-03 18:52:15 gridsearch odte iris 1 0 0.9933330 0.9786560 +
2020-12-07 18:41:58 gridsearch odte led-display 1 0 0.7310000 0.7102000 +
2021-01-09 4:17:20 gridsearch odte libras 1 0 0.8027780 0.8911110 -
2020-12-23 8:03:20 gridsearch odte low-res-spect 1 0 0.9134020 0.9028200 +
2020-11-22 6:38:17 gridsearch odte lymphography 1 0 0.8917240 0.8554050 +
2020-12-19 16:13:00 gridsearch odte mammographic 1 0 0.8428760 0.8274720 +
2020-12-12 12:15:28 gridsearch odte molec-biol-promoter 1 0 0.8961040 0.8182690 +
2020-12-23 4:28:26 gridsearch odte musk-1 1 0 0.8339030 0.8764710 -
2020-12-28 21:05:55 gridsearch odte oocytes_merluccius_nucleus_4d 1 0 0.8434190 0.8399630 +
2020-12-20 2:56:17 gridsearch odte oocytes_merluccius_states_2f 1 0 0.9295120 0.9299630 -
2020-12-08 19:38:34 gridsearch odte oocytes_trisopterus_nucleus_2f 1 0 0.7509640 0.8333330 -
2020-12-20 20:31:07 gridsearch odte oocytes_trisopterus_states_5b 1 0 0.8761360 0.9315790 -
2020-11-22 11:23:41 gridsearch odte parkinsons 1 0 0.8666670 0.9202210 -
2020-12-20 23:16:56 gridsearch odte pima 1 0 0.7890930 0.7671880 +
2020-11-22 13:27:02 gridsearch odte pittsburg-bridges-MATERIAL 1 0 0.8675320 0.8642860 +
2020-11-23 10:03:06 gridsearch odte pittsburg-bridges-REL-L 1 0 0.7052380 0.6959290 +
2020-11-22 13:03:57 gridsearch odte pittsburg-bridges-SPAN 1 0 0.7093570 0.6891300 +
2020-11-23 9:07:13 gridsearch odte pittsburg-bridges-T-OR-D 1 0 0.9023810 0.8743700 +
2020-11-23 3:03:50 gridsearch odte planning 1 0 0.7478980 0.7255790 +
2020-11-22 11:05:12 gridsearch odte post-operative 1 0 0.7222220 0.7117420 +
2020-11-22 16:26:56 gridsearch odte seeds 1 0 0.9619050 0.9563030 +
2020-12-19 17:15:01 gridsearch odte statlog-australian-credit 1 0 0.7000000 0.6782810 +
2020-12-21 0:36:07 gridsearch odte statlog-german-credit 1 0 0.7770000 0.7562000 +
2020-11-23 0:04:28 gridsearch odte statlog-heart 1 0 0.8666670 0.8422990 +
2020-12-08 21:15:04 gridsearch odte statlog-image 1 0 0.9627710 0.9761940 -
2020-12-08 21:36:41 gridsearch odte statlog-vehicle 1 0 0.8179810 0.8006730 +
2020-12-25 6:11:33 gridsearch odte synthetic-control 1 0 0.9950000 0.9903330 +
2020-12-19 2:22:48 gridsearch odte tic-tac-toe 1 0 0.9874350 0.9853850 +
2020-11-23 12:30:11 gridsearch odte vertebral-column-2clases 1 0 0.8451610 0.8491530 -
2020-11-23 1:18:25 gridsearch odte wine 1 0 0.9944440 0.9932810 +
2020-11-22 19:29:44 gridsearch odte zoo 1 0 0.9800000 0.9603850 +
we have better results 37 times
we have worse results 12 times
we have equal results 0 times
stree used 0 times
bagging used 0 times
adaBoost used 0 times
odte used 49 times

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results/antiguos/report.txt Executable file
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******************************************************************************************************
* Best Hyperparameters found for datasets -- any classifier -- *
******************************************************************************************************
Date Time Type Classifier Dataset Nor Std Accuracy Reference
========== ======== ========== ========== ============================== === === ========= ===========
2020-12-23 7:47:46 gridsearch odte balance-scale 1 0 0.9712000 0.9046280 +
2020-11-21 23:48:05 gridsearch adaBoost balloons 1 0 0.8833330 0.6625000 +
2020-12-16 22:24:05 gridsearch odte breast-cancer-wisc-diag 1 0 0.9824560 0.9743450 +
2020-12-16 20:10:07 gridsearch odte breast-cancer-wisc-prog 1 0 0.8487180 0.7993400 +
2020-11-23 12:10:11 gridsearch bagging breast-cancer-wisc 1 0 0.9771220 0.9702560 +
2020-11-19 23:44:41 gridsearch bagging breast-cancer 1 1 0.7519060 0.7382400 +
2020-12-07 23:56:19 gridsearch odte cardiotocography-10clases 1 0 0.7516510 0.8277610 -
2020-12-07 16:13:18 gridsearch odte cardiotocography-3clases 1 0 0.8612550 0.9201340 -
2020-12-21 13:58:11 gridsearch odte conn-bench-sonar-mines-rocks 1 0 0.7550520 0.8336540 -
2020-12-18 5:26:37 gridsearch odte cylinder-bands 1 0 0.6699600 0.7691410 -
2020-12-17 18:10:44 gridsearch odte dermatology 1 0 0.9864120 0.9732780 +
2020-11-22 5:37:43 gridsearch odte echocardiogram 1 0 0.8698010 0.8485270 +
2020-11-26 0:18:42 gridsearch odte fertility 1 0 0.8900000 0.8840000 +
2020-11-22 21:36:33 gridsearch odte haberman-survival 1 0 0.7745640 0.7392540 +
2020-11-23 15:28:21 gridsearch bagging heart-hungarian 1 0 0.8471070 0.8204750 +
2020-11-22 4:30:37 gridsearch odte hepatitis 1 0 0.8774190 0.8232030 +
2020-11-13 12:04:28 crossval stree ilpd-indian-liver 1 0 0.7426910 0.7150280 +
2020-11-20 7:39:16 gridsearch bagging ionosphere 1 1 0.9600810 0.9442150 +
2020-11-03 18:52:15 gridsearch odte iris 1 0 0.9933330 0.9786560 +
2020-12-07 18:41:58 gridsearch odte led-display 1 0 0.7310000 0.7102000 +
2021-01-14 0:28:42 gridsearch bagging libras 1 0 0.8194440 0.8911110 -
2020-12-23 8:03:20 gridsearch odte low-res-spect 1 0 0.9134020 0.9028200 +
2020-11-20 1:24:35 gridsearch bagging lymphography 1 1 0.8919540 0.8554050 +
2020-12-19 16:13:00 gridsearch odte mammographic 1 0 0.8428760 0.8274720 +
2020-11-20 2:26:50 gridsearch bagging molec-biol-promoter 1 1 0.9428570 0.8182690 +
2020-12-23 4:28:26 gridsearch odte musk-1 1 0 0.8339030 0.8764710 -
2020-12-28 21:05:55 gridsearch odte oocytes_merluccius_nucleus_4d 1 0 0.8434190 0.8399630 +
2020-12-20 2:56:17 gridsearch odte oocytes_merluccius_states_2f 1 0 0.9295120 0.9299630 -
2020-12-08 19:38:34 gridsearch odte oocytes_trisopterus_nucleus_2f 1 0 0.7509640 0.8333330 -
2020-12-20 20:31:07 gridsearch odte oocytes_trisopterus_states_5b 1 0 0.8761360 0.9315790 -
2020-11-22 11:23:41 gridsearch odte parkinsons 1 0 0.8666670 0.9202210 -
2020-12-20 23:16:56 gridsearch odte pima 1 0 0.7890930 0.7671880 +
2020-11-13 12:15:41 crossval stree pittsburg-bridges-MATERIAL 1 0 0.8861470 0.8642860 +
2020-11-23 10:03:06 gridsearch odte pittsburg-bridges-REL-L 1 0 0.7052380 0.6959290 +
2020-11-21 23:56:48 gridsearch adaBoost pittsburg-bridges-SPAN 1 0 0.7321640 0.6891300 +
2020-11-13 12:15:41 crossval stree pittsburg-bridges-T-OR-D 1 0 0.9023810 0.8743700 +
2020-11-23 3:03:50 gridsearch odte planning 1 0 0.7478980 0.7255790 +
2020-11-13 12:15:41 crossval stree post-operative 1 0 0.7222220 0.7117420 +
2020-11-13 12:15:41 crossval stree seeds 1 0 0.9619050 0.9563030 +
2020-12-19 17:15:01 gridsearch odte statlog-australian-credit 1 0 0.7000000 0.6782810 +
2020-12-21 0:36:07 gridsearch odte statlog-german-credit 1 0 0.7770000 0.7562000 +
2020-11-23 0:04:28 gridsearch odte statlog-heart 1 0 0.8666670 0.8422990 +
2020-12-08 21:15:04 gridsearch odte statlog-image 1 0 0.9627710 0.9761940 -
2020-12-08 21:36:41 gridsearch odte statlog-vehicle 1 0 0.8179810 0.8006730 +
2020-12-25 19:30:56 gridsearch bagging synthetic-control 1 0 0.9966670 0.9903330 +
2020-11-13 12:21:13 crossval stree tic-tac-toe 1 0 0.9874350 0.9853850 +
2020-11-23 12:30:11 gridsearch odte vertebral-column-2clases 1 0 0.8451610 0.8491530 -
2020-11-23 1:18:25 gridsearch odte wine 1 0 0.9944440 0.9932810 +
2020-11-22 19:29:44 gridsearch odte zoo 1 0 0.9800000 0.9603850 +
we have better results 37 times
we have worse results 12 times
we have equal results 0 times
stree used 6 times
bagging used 8 times
adaBoost used 2 times
odte used 33 times

62
results/antiguos/stree.txt Executable file
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@@ -0,0 +1,62 @@
******************************************************************************************************
* Best Hyperparameters found for datasets -- stree classifier -- *
******************************************************************************************************
Date Time Type Classifier Dataset Nor Std Accuracy Reference
========== ======== ========== ========== ============================== === === ========= ===========
2020-11-13 12:04:24 crossval stree balance-scale 1 0 0.9488000 0.9046280 +
2020-11-13 12:04:25 crossval stree balloons 1 0 0.8666670 0.6625000 +
2020-11-13 12:04:25 crossval stree breast-cancer-wisc-diag 1 0 0.9789320 0.9743450 +
2020-11-13 12:04:25 crossval stree breast-cancer-wisc-prog 1 0 0.8284620 0.7993400 +
2020-11-13 12:04:25 crossval stree breast-cancer-wisc 1 0 0.9656940 0.9702560 -
2020-11-13 12:04:26 crossval stree breast-cancer 1 0 0.7308530 0.7382400 -
2020-11-13 12:04:26 crossval stree cardiotocography-10clases 1 0 0.6665220 0.8277610 -
2020-11-13 12:04:27 crossval stree cardiotocography-3clases 1 0 0.8480740 0.9201340 -
2020-12-04 0:42:29 gridsearch stree conn-bench-sonar-mines-rocks 1 0 0.6882690 0.8336540 -
2020-11-13 12:04:27 crossval stree cylinder-bands 1 0 0.6326670 0.7691410 -
2020-11-13 12:04:27 crossval stree dermatology 1 0 0.9754540 0.9732780 +
2020-11-13 12:04:27 crossval stree echocardiogram 1 0 0.8472930 0.8485270 -
2020-11-13 12:04:27 crossval stree fertility 1 0 0.8800000 0.8840000 -
2020-11-13 12:04:28 crossval stree haberman-survival 1 0 0.7646750 0.7392540 +
2020-11-13 12:04:28 crossval stree heart-hungarian 1 0 0.8299240 0.8204750 +
2020-11-13 12:04:28 crossval stree hepatitis 1 0 0.8645160 0.8232030 +
2020-11-13 12:04:28 crossval stree ilpd-indian-liver 1 0 0.7426910 0.7150280 +
2020-11-13 12:04:28 crossval stree ionosphere 1 0 0.9487320 0.9442150 +
2020-11-13 12:04:28 crossval stree iris 0 0 0.9800000 0.9786560 +
2020-11-13 12:15:39 crossval stree led-display 1 0 0.7120000 0.7102000 +
2020-12-10 1:28:06 gridsearch stree libras 1 0 0.7027780 0.8911110 -
2020-12-09 23:13:34 gridsearch stree low-res-spect 1 0 0.8794920 0.9028200 -
2020-11-13 12:15:39 crossval stree lymphography 1 0 0.8648280 0.8554050 +
2020-11-13 12:15:39 crossval stree mammographic 1 0 0.8293720 0.8274720 +
2020-12-03 19:16:25 gridsearch stree molec-biol-promoter 1 0 0.8108220 0.8182690 -
2020-12-11 12:21:33 gridsearch stree musk-1 1 0 0.7899120 0.8764710 -
2020-11-13 12:15:39 crossval stree oocytes_merluccius_nucleus_4d 1 0 0.8082210 0.8399630 -
2020-11-13 12:15:39 crossval stree oocytes_merluccius_states_2f 1 0 0.9119030 0.9299630 -
2020-11-13 12:15:40 crossval stree oocytes_trisopterus_nucleus_2f 1 0 0.7476910 0.8333330 -
2020-11-13 12:15:40 crossval stree oocytes_trisopterus_states_5b 1 0 0.8453610 0.9315790 -
2020-11-13 12:15:40 crossval stree parkinsons 1 0 0.8461540 0.9202210 -
2020-11-13 12:15:41 crossval stree pima 1 0 0.7800020 0.7671880 +
2020-11-13 12:15:41 crossval stree pittsburg-bridges-MATERIAL 1 0 0.8861470 0.8642860 +
2020-11-13 12:15:41 crossval stree pittsburg-bridges-REL-L 1 0 0.6761900 0.6959290 -
2020-11-13 12:15:41 crossval stree pittsburg-bridges-SPAN 1 0 0.6771930 0.6891300 -
2020-11-13 12:15:41 crossval stree pittsburg-bridges-T-OR-D 1 0 0.9023810 0.8743700 +
2020-11-13 12:15:41 crossval stree planning 1 0 0.7255250 0.7255790 -
2020-11-13 12:15:41 crossval stree post-operative 1 0 0.7222220 0.7117420 +
2020-11-13 12:15:41 crossval stree seeds 1 0 0.9619050 0.9563030 +
2020-11-13 12:15:41 crossval stree statlog-australian-credit 1 0 0.6797100 0.6782810 +
2020-11-13 12:21:08 crossval stree statlog-german-credit 1 0 0.7620000 0.7562000 +
2020-11-13 12:21:08 crossval stree statlog-heart 1 0 0.8481480 0.8422990 +
2020-11-13 12:21:12 crossval stree statlog-image 1 0 0.9593070 0.9761940 -
2020-11-13 12:21:13 crossval stree statlog-vehicle 1 0 0.8014130 0.8006730 +
2020-12-07 10:42:19 gridsearch stree synthetic-control 1 0 0.9716670 0.9903330 -
2020-11-13 12:21:13 crossval stree tic-tac-toe 1 0 0.9874350 0.9853850 +
2020-11-13 12:21:14 crossval stree vertebral-column-2clases 1 0 0.8290320 0.8491530 -
2020-11-13 12:21:14 crossval stree wine 1 0 0.9777780 0.9932810 -
2020-11-13 12:21:14 crossval stree zoo 1 0 0.9704760 0.9603850 +
we have better results 25 times
we have worse results 24 times
we have equal results 0 times
stree used 49 times
bagging used 0 times
adaBoost used 0 times
odte used 0 times

49
results/base_raf.sql Normal file
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@@ -0,0 +1,49 @@
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:20:51', 'crossval', 0.706738, 'balance-scale', 'BaseRaF', 1, 0, '{}', 0.230848, 0.0318996, 0.0178062, 28.81);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:20:52', 'crossval', 0.605, 'balloons', 'BaseRaF', 1, 0, '{}', 0.230195, 0.0011394, 0.000448618, 4.65);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:21:06', 'crossval', 0.965694, 'breast-cancer-wisc-diag', 'BaseRaF', 1, 0, '{}', 0.0143465, 0.0149353, 0.00289327, 10.81);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:21:12', 'crossval', 0.74485, 'breast-cancer-wisc-prog', 'BaseRaF', 1, 0, '{}', 0.0541356, 0.00703754, 0.000955761, 16.44);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:21:27', 'crossval', 0.942857, 'breast-cancer-wisc', 'BaseRaF', 1, 0, '{}', 0.0166086, 0.0151967, 0.0017186, 28.89);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:21:40', 'crossval', 0.656438, 'breast-cancer', 'BaseRaF', 1, 0, '{}', 0.0500531, 0.0192996, 0.00201731, 64.88);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:25:14', 'crossval', 0.774788, 'cardiotocography-10clases', 'BaseRaF', 1, 0, '{}', 0.0179435, 0.42372, 0.111354, 255.01);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:27:36', 'crossval', 0.896715, 'cardiotocography-3clases', 'BaseRaF', 1, 0, '{}', 0.0129992, 0.381444, 0.0666812, 106.66);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:27:41', 'crossval', 0.772981, 'conn-bench-sonar-mines-rocks', 'BaseRaF', 1, 0, '{}', 0.054519, 0.00598101, 0.000759466, 13.5);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:02', 'crossval', 0.675117, 'cylinder-bands', 'BaseRaF', 1, 0, '{}', 0.0359021, 0.0249964, 0.00202589, 47.54);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:13', 'crossval', 0.970723, 'dermatology', 'BaseRaF', 1, 0, '{}', 0.0175846, 0.0121389, 0.0010901, 13.93);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:18', 'crossval', 0.753522, 'echocardiogram', 'BaseRaF', 1, 0, '{}', 0.0718413, 0.00750033, 0.00161811, 20.14);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:21', 'crossval', 0.798, 'fertility', 'BaseRaF', 1, 0, '{}', 0.0787592, 0.00438735, 0.00082982, 15.92);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:31', 'crossval', 0.720133, 'haberman-survival', 'BaseRaF', 1, 0, '{}', 0.0475346, 0.0131448, 0.00499674, 32.68);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:43', 'crossval', 0.779804, 'heart-hungarian', 'BaseRaF', 1, 0, '{}', 0.0445811, 0.0166187, 0.00157407, 50.97);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:47', 'crossval', 0.773671, 'hepatitis', 'BaseRaF', 1, 0, '{}', 0.0639841, 0.00578021, 0.000831726, 16.34);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:29:13', 'crossval', 0.696685, 'ilpd-indian-liver', 'BaseRaF', 1, 0, '{}', 0.0356957, 0.0304009, 0.010731, 78.23);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:29:22', 'crossval', 0.875389, 'ionosphere', 'BaseRaF', 1, 0, '{}', 0.0343367, 0.00810207, 0.00126368, 14.86);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:29:25', 'crossval', 0.953413, 'iris', 'BaseRaF', 1, 0, '{}', 0.0312751, 0.00319496, 0.000564433, 10.65);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:30:28', 'crossval', 0.70178, 'led-display', 'BaseRaF', 1, 0, '{}', 0.0236684, 0.0837334, 0.00515429, 133.27);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:31:24', 'crossval', 0.726722, 'libras', 'BaseRaF', 1, 0, '{}', 0.0462516, 0.194452, 0.0319483, 65);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:31:49', 'crossval', 0.790875, 'low-res-spect', 'BaseRaF', 1, 0, '{}', 0.0338948, 0.0700037, 0.00737621, 22.25);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:31:54', 'crossval', 0.761622, 'lymphography', 'BaseRaF', 1, 0, '{}', 0.072691, 0.00602972, 0.000858306, 17.09);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:32:50', 'crossval', 0.780206, 'mammographic', 'BaseRaF', 1, 0, '{}', 0.0261463, 0.0725665, 0.00665848, 177.33);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:32:52', 'crossval', 0.667239, 'molec-biol-promoter', 'BaseRaF', 1, 0, '{}', 0.0894839, 0.00204324, 0.000882715, 4.23);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:33:10', 'crossval', 0.834034, 'musk-1', 'BaseRaF', 1, 0, '{}', 0.0355503, 0.0294025, 0.00582493, 32.15);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:33:59', 'crossval', 0.792313, 'oocytes_merluccius_nucleus_4d', 'BaseRaF', 1, 0, '{}', 0.0236722, 0.0609631, 0.00810882, 76.76);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:34:47', 'crossval', 0.910551, 'oocytes_merluccius_states_2f', 'BaseRaF', 1, 0, '{}', 0.0178087, 0.0589824, 0.00537782, 46.61);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:35:27', 'crossval', 0.76193, 'oocytes_trisopterus_nucleus_2f', 'BaseRaF', 1, 0, '{}', 0.0290605, 0.0437252, 0.00599953, 84.37);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:36:01', 'crossval', 0.922149, 'oocytes_trisopterus_states_5b', 'BaseRaF', 1, 0, '{}', 0.016058, 0.0338147, 0.0038348, 28.44);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:36:06', 'crossval', 0.87924, 'parkinsons', 'BaseRaF', 1, 0, '{}', 0.0434764, 0.00664538, 0.000956323, 18.21);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:36:50', 'crossval', 0.697005, 'pima', 'BaseRaF', 1, 0, '{}', 0.0329339, 0.0598898, 0.00405292, 179.02);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:36:54', 'crossval', 0.81136, 'pittsburg-bridges-MATERIAL', 'BaseRaF', 1, 0, '{}', 0.0721078, 0.00614998, 0.000956048, 18.21);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:36:59', 'crossval', 0.622107, 'pittsburg-bridges-REL-L', 'BaseRaF', 1, 0, '{}', 0.0836926, 0.0107519, 0.00245178, 33.92);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:04', 'crossval', 0.630217, 'pittsburg-bridges-SPAN', 'BaseRaF', 1, 0, '{}', 0.0855243, 0.0103792, 0.00144035, 32.74);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:07', 'crossval', 0.821007, 'pittsburg-bridges-T-OR-D', 'BaseRaF', 1, 0, '{}', 0.071734, 0.00451751, 0.000865693, 15.6);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:16', 'crossval', 0.590586, 'planning', 'BaseRaF', 1, 0, '{}', 0.0720305, 0.0149104, 0.00202668, 49.52);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:21', 'crossval', 0.539375, 'post-operative', 'BaseRaF', 1, 0, '{}', 0.102817, 0.00860377, 0.00125772, 28.27);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:25', 'crossval', 0.942518, 'seeds', 'BaseRaF', 1, 0, '{}', 0.0293366, 0.00587637, 0.000679482, 14.71);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:27', 'crossval', 0.678261, 'statlog-australian-credit', 'BaseRaF', 1, 0, '{}', 0.0319213, 0.00851742, 0.00101665, 1);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:38:23', 'crossval', 0.68762, 'statlog-german-credit', 'BaseRaF', 1, 0, '{}', 0.0285632, 0.0759742, 0.00988248, 118.37);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:38:33', 'crossval', 0.747605, 'statlog-heart', 'BaseRaF', 1, 0, '{}', 0.0505201, 0.01431, 0.00186931, 34.9);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:41:32', 'crossval', 0.953604, 'statlog-image', 'BaseRaF', 1, 0, '{}', 0.0428999, 0.459961, 0.209446, 91.85);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:42:14', 'crossval', 0.789572, 'statlog-vehicle', 'BaseRaF', 1, 0, '{}', 0.0290904, 0.0418569, 0.00252999, 79.05);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:42:32', 'crossval', 0.971567, 'synthetic-control', 'BaseRaF', 1, 0, '{}', 0.0148618, 0.0233277, 0.00241783, 15.44);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:42:58', 'crossval', 0.974906, 'tic-tac-toe', 'BaseRaF', 1, 0, '{}', 0.0119599, 0.0372124, 0.00723869, 21.49);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:43:10', 'crossval', 0.822601, 'vertebral-column-2clases', 'BaseRaF', 1, 0, '{}', 0.0397492, 0.016375, 0.00309965, 45.54);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:43:13', 'crossval', 0.97748, 'wine', 'BaseRaF', 1, 0, '{}', 0.020518, 0.00259591, 0.000401448, 6.86);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:43:15', 'crossval', 0.936262, 'zoo', 'BaseRaF', 1, 0, '{}', 0.0494478, 0.00416789, 0.00034269, 13.2);

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replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:20:51', 'crossval', 0.706738, 'balance-scale', 'BaseRaF', 1, 0, '{}', 0.230848, 0.0318996, 0.0178062, 28.81);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:20:52', 'crossval', 0.605, 'balloons', 'BaseRaF', 1, 0, '{}', 0.230195, 0.0011394, 0.000448618, 4.65);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:21:06', 'crossval', 0.965694, 'breast-cancer-wisc-diag', 'BaseRaF', 1, 0, '{}', 0.0143465, 0.0149353, 0.00289327, 10.81);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:21:12', 'crossval', 0.74485, 'breast-cancer-wisc-prog', 'BaseRaF', 1, 0, '{}', 0.0541356, 0.00703754, 0.000955761, 16.44);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:21:27', 'crossval', 0.942857, 'breast-cancer-wisc', 'BaseRaF', 1, 0, '{}', 0.0166086, 0.0151967, 0.0017186, 28.89);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:21:40', 'crossval', 0.656438, 'breast-cancer', 'BaseRaF', 1, 0, '{}', 0.0500531, 0.0192996, 0.00201731, 64.88);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:25:14', 'crossval', 0.774788, 'cardiotocography-10clases', 'BaseRaF', 1, 0, '{}', 0.0179435, 0.42372, 0.111354, 255.01);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:27:36', 'crossval', 0.896715, 'cardiotocography-3clases', 'BaseRaF', 1, 0, '{}', 0.0129992, 0.381444, 0.0666812, 106.66);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:27:41', 'crossval', 0.772981, 'conn-bench-sonar-mines-rocks', 'BaseRaF', 1, 0, '{}', 0.054519, 0.00598101, 0.000759466, 13.5);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:02', 'crossval', 0.675117, 'cylinder-bands', 'BaseRaF', 1, 0, '{}', 0.0359021, 0.0249964, 0.00202589, 47.54);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:13', 'crossval', 0.970723, 'dermatology', 'BaseRaF', 1, 0, '{}', 0.0175846, 0.0121389, 0.0010901, 13.93);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:18', 'crossval', 0.753522, 'echocardiogram', 'BaseRaF', 1, 0, '{}', 0.0718413, 0.00750033, 0.00161811, 20.14);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:21', 'crossval', 0.798, 'fertility', 'BaseRaF', 1, 0, '{}', 0.0787592, 0.00438735, 0.00082982, 15.92);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:31', 'crossval', 0.720133, 'haberman-survival', 'BaseRaF', 1, 0, '{}', 0.0475346, 0.0131448, 0.00499674, 32.68);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:43', 'crossval', 0.779804, 'heart-hungarian', 'BaseRaF', 1, 0, '{}', 0.0445811, 0.0166187, 0.00157407, 50.97);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:47', 'crossval', 0.773671, 'hepatitis', 'BaseRaF', 1, 0, '{}', 0.0639841, 0.00578021, 0.000831726, 16.34);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:29:13', 'crossval', 0.696685, 'ilpd-indian-liver', 'BaseRaF', 1, 0, '{}', 0.0356957, 0.0304009, 0.010731, 78.23);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:29:22', 'crossval', 0.875389, 'ionosphere', 'BaseRaF', 1, 0, '{}', 0.0343367, 0.00810207, 0.00126368, 14.86);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:29:25', 'crossval', 0.953413, 'iris', 'BaseRaF', 1, 0, '{}', 0.0312751, 0.00319496, 0.000564433, 10.65);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:30:28', 'crossval', 0.70178, 'led-display', 'BaseRaF', 1, 0, '{}', 0.0236684, 0.0837334, 0.00515429, 133.27);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:31:24', 'crossval', 0.726722, 'libras', 'BaseRaF', 1, 0, '{}', 0.0462516, 0.194452, 0.0319483, 65);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:31:49', 'crossval', 0.790875, 'low-res-spect', 'BaseRaF', 1, 0, '{}', 0.0338948, 0.0700037, 0.00737621, 22.25);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:31:54', 'crossval', 0.761622, 'lymphography', 'BaseRaF', 1, 0, '{}', 0.072691, 0.00602972, 0.000858306, 17.09);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:32:50', 'crossval', 0.780206, 'mammographic', 'BaseRaF', 1, 0, '{}', 0.0261463, 0.0725665, 0.00665848, 177.33);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:32:52', 'crossval', 0.667239, 'molec-biol-promoter', 'BaseRaF', 1, 0, '{}', 0.0894839, 0.00204324, 0.000882715, 4.23);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:33:10', 'crossval', 0.834034, 'musk-1', 'BaseRaF', 1, 0, '{}', 0.0355503, 0.0294025, 0.00582493, 32.15);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:33:59', 'crossval', 0.792313, 'oocytes_merluccius_nucleus_4d', 'BaseRaF', 1, 0, '{}', 0.0236722, 0.0609631, 0.00810882, 76.76);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:34:47', 'crossval', 0.910551, 'oocytes_merluccius_states_2f', 'BaseRaF', 1, 0, '{}', 0.0178087, 0.0589824, 0.00537782, 46.61);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:35:27', 'crossval', 0.76193, 'oocytes_trisopterus_nucleus_2f', 'BaseRaF', 1, 0, '{}', 0.0290605, 0.0437252, 0.00599953, 84.37);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:36:01', 'crossval', 0.922149, 'oocytes_trisopterus_states_5b', 'BaseRaF', 1, 0, '{}', 0.016058, 0.0338147, 0.0038348, 28.44);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:36:06', 'crossval', 0.87924, 'parkinsons', 'BaseRaF', 1, 0, '{}', 0.0434764, 0.00664538, 0.000956323, 18.21);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:36:50', 'crossval', 0.697005, 'pima', 'BaseRaF', 1, 0, '{}', 0.0329339, 0.0598898, 0.00405292, 179.02);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:36:54', 'crossval', 0.81136, 'pittsburg-bridges-MATERIAL', 'BaseRaF', 1, 0, '{}', 0.0721078, 0.00614998, 0.000956048, 18.21);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:36:59', 'crossval', 0.622107, 'pittsburg-bridges-REL-L', 'BaseRaF', 1, 0, '{}', 0.0836926, 0.0107519, 0.00245178, 33.92);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:04', 'crossval', 0.630217, 'pittsburg-bridges-SPAN', 'BaseRaF', 1, 0, '{}', 0.0855243, 0.0103792, 0.00144035, 32.74);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:07', 'crossval', 0.821007, 'pittsburg-bridges-T-OR-D', 'BaseRaF', 1, 0, '{}', 0.071734, 0.00451751, 0.000865693, 15.6);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:16', 'crossval', 0.590586, 'planning', 'BaseRaF', 1, 0, '{}', 0.0720305, 0.0149104, 0.00202668, 49.52);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:21', 'crossval', 0.539375, 'post-operative', 'BaseRaF', 1, 0, '{}', 0.102817, 0.00860377, 0.00125772, 28.27);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:25', 'crossval', 0.942518, 'seeds', 'BaseRaF', 1, 0, '{}', 0.0293366, 0.00587637, 0.000679482, 14.71);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:27', 'crossval', 0.678261, 'statlog-australian-credit', 'BaseRaF', 1, 0, '{}', 0.0319213, 0.00851742, 0.00101665, 1);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:38:23', 'crossval', 0.68762, 'statlog-german-credit', 'BaseRaF', 1, 0, '{}', 0.0285632, 0.0759742, 0.00988248, 118.37);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:38:33', 'crossval', 0.747605, 'statlog-heart', 'BaseRaF', 1, 0, '{}', 0.0505201, 0.01431, 0.00186931, 34.9);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:41:32', 'crossval', 0.953604, 'statlog-image', 'BaseRaF', 1, 0, '{}', 0.0428999, 0.459961, 0.209446, 91.85);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:42:14', 'crossval', 0.789572, 'statlog-vehicle', 'BaseRaF', 1, 0, '{}', 0.0290904, 0.0418569, 0.00252999, 79.05);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:42:32', 'crossval', 0.971567, 'synthetic-control', 'BaseRaF', 1, 0, '{}', 0.0148618, 0.0233277, 0.00241783, 15.44);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:42:58', 'crossval', 0.974906, 'tic-tac-toe', 'BaseRaF', 1, 0, '{}', 0.0119599, 0.0372124, 0.00723869, 21.49);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:43:10', 'crossval', 0.822601, 'vertebral-column-2clases', 'BaseRaF', 1, 0, '{}', 0.0397492, 0.016375, 0.00309965, 45.54);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:43:13', 'crossval', 0.97748, 'wine', 'BaseRaF', 1, 0, '{}', 0.020518, 0.00259591, 0.000401448, 6.86);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:43:15', 'crossval', 0.936262, 'zoo', 'BaseRaF', 1, 0, '{}', 0.0494478, 0.00416789, 0.00034269, 13.2);

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results/no_normalizados/stree.txt Normal file
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* Process all datasets set with stree: tanveer norm: True std: False store in: stree
5 Fold Cross Validation with 10 random seeds [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
Dataset Samp Var Cls Nodes Leaves Depth Accuracy Time Parameters
============================== ===== === === ======= ======= ======= =============== =============== ==========
balance-scale 625 4 3 7.00 4.00 3.00 0.970560±0.0150 0.013607±0.0012 {"C": 10000.0, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}
balloons 16 4 2 3.00 2.00 2.00 0.860000±0.2850 0.000802±0.0001 {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}
breast-cancer-wisc-diag 569 30 2 3.24 2.12 2.12 0.972764±0.0173 0.003826±0.0007 {"C": 0.2, "max_iter": 10000.0}
breast-cancer-wisc-prog 198 33 2 5.84 3.42 3.24 0.811128±0.0585 0.007777±0.0015 {"C": 0.2, "max_iter": 10000.0}
breast-cancer-wisc 699 9 2 8.88 4.94 4.08 0.966661±0.0139 0.006625±0.0008 {}
breast-cancer 286 9 2 21.72 11.36 5.86 0.734211±0.0480 0.023730±0.0059 {}
cardiotocography-10clases 2126 21 10 160.76 80.88 22.86 0.791487±0.0192 3.147645±0.3417 {}
cardiotocography-3clases 2126 21 3 47.68 24.34 8.84 0.900613±0.0154 1.070610±0.1435 {}
conn-bench-sonar-mines-rocks 208 60 2 6.08 3.54 2.86 0.755528±0.0678 0.011588±0.0034 {}
cylinder-bands 512 35 2 26.20 13.60 6.82 0.715049±0.0368 0.307926±0.1114 {}
dermatology 366 34 6 11.00 6.00 6.00 0.971833±0.0207 0.038074±0.0108 {"C": 55, "max_iter": 10000.0}
echocardiogram 131 10 2 7.00 4.00 3.54 0.814758±0.0998 0.003269±0.0009 {"C": 7, "gamma": 0.1, "kernel": "poly", "max_features": "auto", "max_iter": 10000.0}
fertility 100 9 2 1.00 1.00 1.00 0.880000±0.0548 0.000961±0.0001 {"C": 0.05, "max_features": "auto", "max_iter": 10000.0}
haberman-survival 306 3 2 23.40 12.20 5.98 0.735637±0.0435 0.017420±0.0034 {}
heart-hungarian 294 12 2 10.16 5.58 4.00 0.827522±0.0505 0.004928±0.0007 {"C": 0.05, "max_iter": 10000.0}
hepatitis 155 19 2 3.00 2.00 2.00 0.824516±0.0739 0.002160±0.0001 {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}
ilpd-indian-liver 583 9 2 16.04 8.52 5.28 0.723498±0.0385 0.035263±0.0160 {}
ionosphere 351 33 2 3.16 2.08 2.08 0.953276±0.0239 0.008915±0.0008 {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}
iris 150 4 3 5.00 3.00 3.00 0.965333±0.0319 0.003556±0.0004 {}
led-display 1000 7 10 47.16 24.08 17.76 0.703000±0.0291 0.225625±0.0119 {}
libras 360 90 15 82.28 41.64 28.84 0.788611±0.0517 0.851997±0.0843 {"C": 0.08, "max_iter": 10000.0}
low-res-spect 531 100 9 27.40 14.20 10.74 0.883782±0.0325 0.450486±0.0413 {"C": 0.05, "max_iter": 10000.0}
lymphography 148 18 4 9.04 5.02 4.48 0.835034±0.0591 0.005482±0.0008 {"C": 0.05, "max_iter": 10000.0}
mammographic 961 5 2 7.40 4.20 4.00 0.819150±0.0223 0.023214±0.0034 {}
molec-biol-promoter 106 57 2 3.00 2.00 2.00 0.767056±0.0911 0.001287±0.0001 {"C": 0.05, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}
musk-1 476 166 2 3.00 2.00 2.00 0.916388±0.0275 0.011613±0.0004 {"C": 0.05, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}
oocytes_merluccius_nucleus_4d 1022 41 2 10.52 5.76 4.42 0.835125±0.0221 0.210538±0.0275 {"C": 8.25, "gamma": 0.1, "kernel": "poly"}
oocytes_merluccius_states_2f 1022 25 3 18.04 9.52 5.30 0.915365±0.0204 0.184491±0.0301 {}
oocytes_trisopterus_nucleus_2f 912 25 2 29.88 15.44 7.38 0.800986±0.0218 0.734998±0.2147 {}
oocytes_trisopterus_states_5b 912 32 3 7.44 4.22 3.60 0.922249±0.0179 0.055960±0.0084 {"C": 0.11, "max_iter": 10000.0}
parkinsons 195 22 2 8.48 4.74 3.76 0.882051±0.0478 0.008070±0.0018 {}
pima 768 8 2 17.40 9.20 5.66 0.766651±0.0297 0.076684±0.0218 {}
pittsburg-bridges-MATERIAL 106 7 3 5.16 3.08 3.02 0.867749±0.0712 0.003015±0.0003 {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}
pittsburg-bridges-REL-L 103 7 3 16.32 8.66 5.96 0.632238±0.1012 0.013737±0.0033 {}
pittsburg-bridges-SPAN 92 7 3 9.84 5.42 4.58 0.659766±0.1165 0.005307±0.0016 {"C": 0.05, "max_iter": 10000.0}
pittsburg-bridges-T-OR-D 102 7 2 4.56 2.78 2.68 0.861619±0.0694 0.003034±0.0006 {}
planning 182 12 2 3.00 2.00 2.00 0.735270±0.0670 0.003102±0.0001 {"C": 7, "gamma": 10.0, "kernel": "rbf", "max_iter": 10000.0}
post-operative 90 8 3 2.64 1.82 1.82 0.711111±0.0754 0.001882±0.0005 {"C": 55, "degree": 5, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}
seeds 210 7 3 9.88 5.44 4.44 0.952857±0.0280 0.020686±0.0053 {"C": 10000.0, "max_iter": 10000.0}
statlog-australian-credit 690 14 2 1.32 1.16 1.16 0.678261±0.0390 0.002094±0.0009 {"C": 0.05, "max_features": "auto", "max_iter": 10000.0}
statlog-german-credit 1000 24 2 21.24 11.12 6.18 0.762500±0.0272 0.298142±0.0660 {}
statlog-heart 270 13 2 14.56 7.78 5.00 0.822963±0.0440 0.014240±0.0033 {}
statlog-image 2310 18 7 36.92 18.96 10.80 0.955931±0.0096 4.345813±0.2017 {"C": 7, "max_iter": 10000.0}
statlog-vehicle 846 18 4 23.88 12.44 7.06 0.793028±0.0301 0.283493±0.0393 {}
synthetic-control 600 60 6 12.48 6.74 6.50 0.950000±0.0254 0.208679±0.0415 {"C": 0.55, "max_iter": 10000.0}
tic-tac-toe 958 9 2 3.00 2.00 2.00 0.984444±0.0084 0.012515±0.0004 {"C": 0.2, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}
vertebral-column-2clases 310 6 2 6.04 3.52 3.34 0.852903±0.0409 0.006154±0.0010 {}
wine 178 13 3 5.00 3.00 3.00 0.979159±0.0224 0.001952±0.0001 {"C": 0.55, "max_iter": 10000.0}
zoo 101 16 7 13.04 7.02 7.02 0.957524±0.0455 0.005572±0.0001 {"C": 0.1, "max_iter": 10000.0}
- Auto Hyperparams ..: {"C": 0.1, "max_iter": 10000.0}
Time: 0h 10m 49s

57
results/no_normalizados/stree_default.txt Normal file
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* Process all datasets set with stree_default: tanveer norm: True std: False store in: stree_default
5 Fold Cross Validation with 10 random seeds [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
Dataset Samp Var Cls Nodes Leaves Depth Accuracy Time Parameters
============================== ===== === === ====== ====== ====== =============== =============== ==========
balance-scale 625 4 3 26.12 13.56 7.94 0.910720±0.0249 0.021917±0.0068 {}
balloons 16 4 2 3.56 2.28 2.14 0.653333±0.2627 0.001299±0.0004 {}
breast-cancer-wisc-diag 569 30 2 5.96 3.48 3.00 0.968898±0.0177 0.007286±0.0017 {}
breast-cancer-wisc-prog 198 33 2 7.08 4.04 3.20 0.802051±0.0542 0.028998±0.0068 {}
breast-cancer-wisc 699 9 2 8.88 4.94 4.08 0.966661±0.0139 0.006549±0.0008 {}
breast-cancer 286 9 2 21.72 11.36 5.86 0.734211±0.0480 0.023676±0.0060 {}
cardiotocography-10clases 2126 21 10 160.76 80.88 22.86 0.791487±0.0192 3.113959±0.3464 {}
cardiotocography-3clases 2126 21 3 47.68 24.34 8.84 0.900613±0.0154 1.051307±0.1414 {}
conn-bench-sonar-mines-rocks 208 60 2 6.08 3.54 2.86 0.755528±0.0678 0.011538±0.0034 {}
cylinder-bands 512 35 2 26.20 13.60 6.82 0.715049±0.0368 0.301428±0.1096 {}
dermatology 366 34 6 11.04 6.02 6.02 0.966087±0.0200 0.013223±0.0007 {}
echocardiogram 131 10 2 9.56 5.28 4.10 0.808832±0.0704 0.006589±0.0023 {}
fertility 100 9 2 2.64 1.82 1.76 0.866000±0.0620 0.002325±0.0010 {}
haberman-survival 306 3 2 23.40 12.20 5.98 0.735637±0.0435 0.017143±0.0034 {}
heart-hungarian 294 12 2 13.60 7.30 4.46 0.817674±0.0512 0.017137±0.0030 {}
hepatitis 155 19 2 11.48 6.24 4.46 0.796129±0.0720 0.008581±0.0024 {}
ilpd-indian-liver 583 9 2 16.04 8.52 5.28 0.723498±0.0385 0.034662±0.0162 {}
ionosphere 351 33 2 8.52 4.76 3.72 0.866056±0.0369 0.019004±0.0044 {}
iris 150 4 3 5.00 3.00 3.00 0.965333±0.0319 0.003447±0.0004 {}
led-display 1000 7 10 47.16 24.08 17.76 0.703000±0.0291 0.223610±0.0115 {}
libras 360 90 15 55.40 28.20 23.92 0.747778±0.0561 4.877355±0.4811 {}
low-res-spect 531 100 9 23.16 12.08 10.10 0.853102±0.0338 0.818328±0.1122 {}
lymphography 148 18 4 14.84 7.92 5.40 0.773793±0.0767 0.013397±0.0031 {}
mammographic 961 5 2 7.40 4.20 4.00 0.819150±0.0223 0.022731±0.0033 {}
molec-biol-promoter 106 57 2 3.00 2.00 2.00 0.764416±0.0834 0.001472±0.0002 {}
musk-1 476 166 2 6.72 3.86 3.00 0.843463±0.0324 0.254120±0.0505 {}
oocytes_merluccius_nucleus_4d 1022 41 2 15.84 8.42 4.84 0.810657±0.0246 1.538509±0.2465 {}
oocytes_merluccius_states_2f 1022 25 3 18.04 9.52 5.30 0.915365±0.0204 0.181659±0.0291 {}
oocytes_trisopterus_nucleus_2f 912 25 2 29.88 15.44 7.38 0.800986±0.0218 0.716645±0.2090 {}
oocytes_trisopterus_states_5b 912 32 3 9.36 5.18 4.18 0.916655±0.0197 0.346873±0.0956 {}
parkinsons 195 22 2 8.48 4.74 3.76 0.882051±0.0478 0.007959±0.0017 {}
pima 768 8 2 17.40 9.20 5.66 0.766651±0.0297 0.075477±0.0215 {}
pittsburg-bridges-MATERIAL 106 7 3 12.00 6.50 4.82 0.791255±0.0703 0.006945±0.0012 {}
pittsburg-bridges-REL-L 103 7 3 16.32 8.66 5.96 0.632238±0.1012 0.013606±0.0033 {}
pittsburg-bridges-SPAN 92 7 3 13.44 7.22 5.08 0.630234±0.0969 0.008407±0.0019 {}
pittsburg-bridges-T-OR-D 102 7 2 4.56 2.78 2.68 0.861619±0.0694 0.002989±0.0006 {}
planning 182 12 2 3.96 2.48 2.26 0.704550±0.0753 0.010120±0.0048 {}
post-operative 90 8 3 3.72 2.36 2.28 0.675556±0.0898 0.005633±0.0019 {}
seeds 210 7 3 6.60 3.80 3.52 0.949048±0.0372 0.005244±0.0006 {}
statlog-australian-credit 690 14 2 11.04 6.02 4.48 0.667246±0.0386 0.270380±0.1636 {}
statlog-german-credit 1000 24 2 21.24 11.12 6.18 0.762500±0.0272 0.293827±0.0660 {}
statlog-heart 270 13 2 14.56 7.78 5.00 0.822963±0.0440 0.014031±0.0032 {}
statlog-image 2310 18 7 25.48 13.24 9.70 0.952641±0.0077 1.709340±0.4000 {}
statlog-vehicle 846 18 4 23.88 12.44 7.06 0.793028±0.0301 0.279074±0.0386 {}
synthetic-control 600 60 6 12.68 6.84 6.30 0.938833±0.0301 0.364350±0.0736 {}
tic-tac-toe 958 9 2 3.00 2.00 2.00 0.983296±0.0084 0.013774±0.0017 {}
vertebral-column-2clases 310 6 2 6.04 3.52 3.34 0.852903±0.0409 0.005830±0.0008 {}
wine 178 13 3 5.00 3.00 3.00 0.975810±0.0245 0.002039±0.0002 {}
zoo 101 16 7 13.00 7.00 7.00 0.947619±0.0424 0.007747±0.0004 {}
- Auto Hyperparams ..: {}
Time: 0h 14m 2s

49
results/no_normalizados/stree_sin_normalizar.sql Normal file
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replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:30:19','crossval','0.97056','0.015046806970251203','balance-scale','stree','1','0','0.0135215425491333','0.0011360178386564284','{"C": 10000.0, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}','7.0','4.0','3.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:30:19','crossval','0.86','0.28501461950807594','balloons','stree','1','0','0.0008485794067382812','0.0001188045305908689','{"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:30:19','crossval','0.9727635460332246','0.017313211324042233','breast-cancer-wisc-diag','stree','1','0','0.0038550806045532225','0.000617279818182984','{"C": 0.2, "max_iter": 10000.0}','3.24','2.12','2.12');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:30:20','crossval','0.811128205128205','0.05846009894763935','breast-cancer-wisc-prog','stree','1','0','0.007727689743041992','0.001516225580196234','{"C": 0.2, "max_iter": 10000.0}','5.84','3.42','3.24');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:30:20','crossval','0.9666608427543679','0.013942149556406368','breast-cancer-wisc','stree','1','0','0.006568741798400879','0.0007625617496781278','{}','8.88','4.94','4.08');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:30:22','crossval','0.7342105263157894','0.047977358056609264','breast-cancer','stree','1','0','0.023711833953857422','0.005911314725396009','{}','21.72','11.36','5.86');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:32:58','crossval','0.7914874344103837','0.019208232849977837','cardiotocography-10clases','stree','1','0','3.110443158149719','0.3439626255704066','{}','160.76','80.88','22.86');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:33:52','crossval','0.9006125379729356','0.015400444429719303','cardiotocography-3clases','stree','1','0','1.0598349380493164','0.14039412983507724','{}','47.68','24.34','8.84');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:33:52','crossval','0.7555284552845529','0.06784237376406048','conn-bench-sonar-mines-rocks','stree','1','0','0.011767463684082031','0.0034720107334710415','{}','6.08','3.54','2.86');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:34:08','crossval','0.7150485436893205','0.036764625484378664','cylinder-bands','stree','1','0','0.3056864404678345','0.11098936517079835','{}','26.2','13.6','6.82');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:34:10','crossval','0.9718326545723807','0.020688266783058316','dermatology','stree','1','0','0.03817004203796387','0.010806662079855171','{"C": 55, "max_iter": 10000.0}','11.0','6.0','6.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:34:10','crossval','0.8147578347578346','0.09980783640455321','echocardiogram','stree','1','0','0.0032717084884643557','0.0009452149671672354','{"C": 7, "gamma": 0.1, "kernel": "poly", "max_features": "auto", "max_iter": 10000.0}','7.0','4.0','3.54');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:34:11','crossval','0.88','0.0547722557505166','fertility','stree','1','0','0.0008958005905151368','9.260489208604769e-05','{"C": 0.05, "max_features": "auto", "max_iter": 10000.0}','1.0','1.0','1.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:34:12','crossval','0.735637228979376','0.04346136548997221','haberman-survival','stree','1','0','0.017356443405151366','0.003344628768121206','{}','23.4','12.2','5.98');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:34:12','crossval','0.8275219170075979','0.05052827428335672','heart-hungarian','stree','1','0','0.004888038635253906','0.0006866494530326037','{"C": 0.05, "max_iter": 10000.0}','10.16','5.58','4.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:34:12','crossval','0.8245161290322581','0.073887165430815','hepatitis','stree','1','0','0.0021643877029418946','0.00017049195899078594','{"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:34:14','crossval','0.7234983790156204','0.038488555090414656','ilpd-indian-liver','stree','1','0','0.03525063991546631','0.01598720497774422','{}','16.04','8.52','5.28');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:34:15','crossval','0.9532756539235413','0.02385368651558141','ionosphere','stree','1','0','0.008699665069580078','0.0006978980567027729','{"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}','3.16','2.08','2.08');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:34:15','crossval','0.9653333333333333','0.03194439613522917','iris','stree','1','0','0.0035097742080688475','0.0004097695961846504','{}','5.0','3.0','3.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:34:27','crossval','0.7030000000000001','0.029120439557122058','led-display','stree','1','0','0.22439127922058105','0.01169153690154062','{}','47.16','24.08','17.76');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:35:09','crossval','0.7886111111111112','0.05169130237032862','libras','stree','1','0','0.8458884000778198','0.08454762729597892','{"C": 0.08, "max_iter": 10000.0}','82.28','41.64','28.84');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:35:32','crossval','0.8837824016928232','0.032459291468616217','low-res-spect','stree','1','0','0.4472122287750244','0.041499966805103185','{"C": 0.05, "max_iter": 10000.0}','27.4','14.2','10.74');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:35:33','crossval','0.8350344827586206','0.05906491141171708','lymphography','stree','1','0','0.005450162887573242','0.0007729326253123006','{"C": 0.05, "max_iter": 10000.0}','9.04','5.02','4.48');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:35:34','crossval','0.8191504749568224','0.02225166121455892','mammographic','stree','1','0','0.02307891845703125','0.0033052073740422487','{}','7.4','4.2','4.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:35:34','crossval','0.7670562770562769','0.0910922603260364','molec-biol-promoter','stree','1','0','0.0013122034072875976','0.00016183676329530782','{"C": 0.05, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:35:35','crossval','0.9163881578947368','0.027520787090975468','musk-1','stree','1','0','0.011589522361755372','0.0004088059821318706','{"C": 0.05, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:35:46','crossval','0.8351252989000477','0.02209607725442717','oocytes_merluccius_nucleus_4d','stree','1','0','0.20897716522216797','0.027290338397606986','{"C": 8.25, "gamma": 0.1, "kernel": "poly"}','10.52','5.76','4.42');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:35:55','crossval','0.9153653754184602','0.02039598729685399','oocytes_merluccius_states_2f','stree','1','0','0.18353436946868895','0.029760481425220444','{}','18.04','9.52','5.3');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:36:31','crossval','0.8009860085269921','0.0218449453461112','oocytes_trisopterus_nucleus_2f','stree','1','0','0.7218273735046387','0.21090495643818924','{}','29.88','15.44','7.38');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:36:34','crossval','0.9222488440521228','0.017920300227679892','oocytes_trisopterus_states_5b','stree','1','0','0.055391054153442386','0.008478615016401354','{"C": 0.11, "max_iter": 10000.0}','7.44','4.22','3.6');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:36:35','crossval','0.882051282051282','0.04783271309276316','parkinsons','stree','1','0','0.008035006523132325','0.0017704174142100634','{}','8.48','4.74','3.76');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:36:39','crossval','0.7666513878278585','0.02972027059401136','pima','stree','1','0','0.0765744400024414','0.021707739325980017','{}','17.4','9.2','5.66');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:36:39','crossval','0.8677489177489177','0.07122264688853039','pittsburg-bridges-MATERIAL','stree','1','0','0.0029404354095458984','0.00026968464971181453','{"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}','5.16','3.08','3.02');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:36:40','crossval','0.6322380952380952','0.1012105974392682','pittsburg-bridges-REL-L','stree','1','0','0.013614563941955567','0.0032556997108539065','{}','16.32','8.66','5.96');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:36:41','crossval','0.659766081871345','0.11650021102545294','pittsburg-bridges-SPAN','stree','1','0','0.005315670967102051','0.0016026664731376263','{"C": 0.05, "max_iter": 10000.0}','9.84','5.42','4.58');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:36:41','crossval','0.8616190476190478','0.06937466062918769','pittsburg-bridges-T-OR-D','stree','1','0','0.003019518852233887','0.0005686500618931248','{}','4.56','2.78','2.68');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:36:41','crossval','0.7352702702702704','0.06697755238925641','planning','stree','1','0','0.0030538272857666016','0.00016792938405634755','{"C": 7, "gamma": 10.0, "kernel": "rbf", "max_iter": 10000.0}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:36:42','crossval','0.711111111111111','0.07535922203472521','post-operative','stree','1','0','0.001947765350341797','0.000514724238125775','{"C": 55, "degree": 5, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}','2.64','1.82','1.82');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:36:43','crossval','0.9528571428571427','0.0279658035429067','seeds','stree','1','0','0.02068638801574707','0.005372199057747094','{"C": 10000.0, "max_iter": 10000.0}','9.88','5.44','4.44');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:36:43','crossval','0.6782608695652174','0.03904983647915211','statlog-australian-credit','stree','1','0','0.0020897960662841796','0.0008477983008284613','{"C": 0.05, "max_features": "auto", "max_iter": 10000.0}','1.32','1.16','1.16');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:36:58','crossval','0.7625','0.02718915224864506','statlog-german-credit','stree','1','0','0.29993834018707277','0.0654828091768462','{}','21.24','11.12','6.18');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:36:59','crossval','0.8229629629629629','0.044003990341567836','statlog-heart','stree','1','0','0.01415153980255127','0.003446367182544689','{}','14.56','7.78','5.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:40:36','crossval','0.9559307359307361','0.00956073126474503','statlog-image','stree','1','0','4.323495583534241','0.20372333182093552','{"C": 7, "max_iter": 10000.0}','36.92','18.96','10.8');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:40:50','crossval','0.7930281935259312','0.03010396812322711','statlog-vehicle','stree','1','0','0.2877680444717407','0.040200186739421113','{}','23.88','12.44','7.06');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:41:01','crossval','0.9500000000000002','0.025385910352879692','synthetic-control','stree','1','0','0.20925597190856934','0.042345414289880805','{"C": 0.55, "max_iter": 10000.0}','12.48','6.74','6.5');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:41:02','crossval','0.9844442626527051','0.008387465200358178','tic-tac-toe','stree','1','0','0.012439990043640136','0.00046189514348666654','{"C": 0.2, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:41:02','crossval','0.8529032258064515','0.04088510843291576','vertebral-column-2clases','stree','1','0','0.00585533618927002','0.0008871302687065003','{}','6.04','3.52','3.34');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:41:03','crossval','0.9791587301587302','0.022426953738041516','wine','stree','1','0','0.0019552993774414064','0.00011510793334965408','{"C": 0.55, "max_iter": 10000.0}','5.0','3.0','3.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-10','23:41:03','crossval','0.9575238095238094','0.04546150348723332','zoo','stree','1','0','0.005614590644836426','0.00018146760407674055','{"C": 0.1, "max_iter": 10000.0}','13.04','7.02','7.02');

56
results/no_normalizados/wodt.txt Normal file
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* Process all datasets set with wodt: tanveer norm: True std: False store in: wodt
5 Fold Cross Validation with 10 random seeds [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
Dataset Samp Var Cls Nodes Leaves Depth Accuracy Time Parameters
============================== ===== === === ======= ======= ======= =============== =============== ==========
balance-scale 625 4 3 75.52 38.26 9.70 0.921280±0.0314 0.198393±0.0218 {}
balloons 16 4 2 4.84 2.92 2.88 0.678333±0.2528 0.004937±0.0019 {}
breast-cancer-wisc-diag 569 30 2 14.72 7.86 6.10 0.965904±0.0143 0.062668±0.0123 {}
breast-cancer-wisc-prog 198 33 2 37.04 19.02 7.76 0.707551±0.0676 0.067855±0.0089 {}
breast-cancer-wisc 699 9 2 33.48 17.24 8.16 0.942351±0.0177 0.119169±0.0161 {}
breast-cancer 286 9 2 415.64 208.32 50.00 0.663249±0.0493 0.216334±0.0219 {}
cardiotocography-10clases 2126 21 10 638.16 319.58 50.00 0.779304±0.0199 1.415190±0.0509 {}
cardiotocography-3clases 2126 21 3 325.12 163.06 45.84 0.901412±0.0160 0.726560±0.0564 {}
conn-bench-sonar-mines-rocks 208 60 2 21.88 11.44 5.86 0.804123±0.0582 0.047346±0.0063 {}
cylinder-bands 512 35 2 75.84 38.42 9.72 0.702545±0.0371 0.175128±0.0158 {}
dermatology 366 34 6 15.28 8.14 6.22 0.964187±0.0166 0.056320±0.0069 {}
echocardiogram 131 10 2 28.64 14.82 7.86 0.742365±0.0802 0.046372±0.0072 {}
fertility 100 9 2 78.04 39.52 35.06 0.804000±0.0677 0.040254±0.0085 {}
haberman-survival 306 3 2 424.36 212.68 50.00 0.664008±0.0485 0.328691±0.0269 {}
heart-hungarian 294 12 2 57.68 29.34 9.20 0.761929±0.0521 0.111243±0.0129 {}
hepatitis 155 19 2 20.32 10.66 6.16 0.768387±0.0838 0.036927±0.0055 {}
ilpd-indian-liver 583 9 2 212.28 106.64 16.96 0.677859±0.0356 0.430718±0.0227 {}
ionosphere 351 33 2 24.60 12.80 6.90 0.879461±0.0386 0.078043±0.0138 {}
iris 150 4 3 13.96 7.48 6.30 0.946667±0.0365 0.031524±0.0068 {}
led-display 1000 7 10 4140.20 2070.60 50.00 0.704700±0.0292 1.788902±0.0435 {}
libras 360 90 15 117.28 59.14 11.56 0.775556±0.0548 0.274540±0.0164 {}
low-res-spect 531 100 9 75.48 38.24 9.38 0.855533±0.0322 0.214980±0.0220 {}
lymphography 148 18 4 22.76 11.88 6.56 0.780046±0.0760 0.042432±0.0070 {}
mammographic 961 5 2 2984.32 1492.66 50.00 0.757957±0.0249 1.290867±0.0647 {}
molec-biol-promoter 106 57 2 8.80 4.90 3.88 0.778918±0.0768 0.016753±0.0042 {}
musk-1 476 166 2 44.52 22.76 7.96 0.841178±0.0345 0.134605±0.0152 {}
oocytes_merluccius_nucleus_4d 1022 41 2 264.52 132.76 16.50 0.734837±0.0261 0.650283±0.0380 {}
oocytes_merluccius_states_2f 1022 25 3 95.84 48.42 12.06 0.902934±0.0221 0.287351±0.0240 {}
oocytes_trisopterus_nucleus_2f 912 25 2 208.64 104.82 14.80 0.749144±0.0360 0.499573±0.0277 {}
oocytes_trisopterus_states_5b 912 32 3 102.32 51.66 12.14 0.888806±0.0214 0.329667±0.0246 {}
parkinsons 195 22 2 17.56 9.28 6.54 0.900513±0.0514 0.035993±0.0053 {}
pima 768 8 2 213.52 107.26 13.40 0.688162±0.0351 0.436215±0.0229 {}
pittsburg-bridges-MATERIAL 106 7 3 77.20 39.10 32.82 0.799481±0.0774 0.049736±0.0105 {}
pittsburg-bridges-REL-L 103 7 3 102.44 51.72 33.62 0.618429±0.0854 0.084359±0.0114 {}
pittsburg-bridges-SPAN 92 7 3 159.64 80.32 47.52 0.587193±0.1161 0.087624±0.0137 {}
pittsburg-bridges-T-OR-D 102 7 2 77.08 39.04 33.58 0.837524±0.0741 0.043616±0.0101 {}
planning 182 12 2 65.68 33.34 9.60 0.568273±0.0874 0.105671±0.0100 {}
post-operative 90 8 3 394.28 197.64 50.00 0.556667±0.1063 0.122731±0.0191 {}
seeds 210 7 3 21.04 11.02 6.38 0.929048±0.0422 0.049382±0.0075 {}
statlog-australian-credit 690 14 2 249.80 125.40 15.34 0.561739±0.0379 0.465245±0.0223 {}
statlog-german-credit 1000 24 2 167.52 84.26 12.08 0.682500±0.0214 0.402345±0.0212 {}
statlog-heart 270 13 2 42.04 21.52 8.18 0.777778±0.0476 0.084237±0.0109 {}
statlog-image 2310 18 7 153.48 77.24 14.96 0.953420±0.0111 0.726321±0.0496 {}
statlog-vehicle 846 18 4 209.84 105.42 15.54 0.729676±0.0301 0.496708±0.0249 {}
synthetic-control 600 60 6 14.16 7.58 4.92 0.978500±0.0145 0.083450±0.0079 {}
tic-tac-toe 958 9 2 55.28 28.14 8.68 0.942268±0.0334 0.212287±0.0517 {}
vertebral-column-2clases 310 6 2 68.68 34.84 12.62 0.807097±0.0562 0.139532±0.0144 {}
wine 178 13 3 6.28 3.64 3.32 0.967921±0.0282 0.018951±0.0037 {}
zoo 101 16 7 14.12 7.56 5.28 0.951429±0.0437 0.025042±0.0025 {}
- Auto Hyperparams ..: {}
Time: 0h 11m 18s

49
results/normalizados/base_raf.sql Normal file
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replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:20:51', 'crossval', 0.706738, 'balance-scale', 'BaseRaF', 1, 0, '{}', 0.230848, 0.0318996, 0.0178062, 28.81);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:20:52', 'crossval', 0.605, 'balloons', 'BaseRaF', 1, 0, '{}', 0.230195, 0.0011394, 0.000448618, 4.65);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:21:06', 'crossval', 0.965694, 'breast-cancer-wisc-diag', 'BaseRaF', 1, 0, '{}', 0.0143465, 0.0149353, 0.00289327, 10.81);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:21:12', 'crossval', 0.74485, 'breast-cancer-wisc-prog', 'BaseRaF', 1, 0, '{}', 0.0541356, 0.00703754, 0.000955761, 16.44);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:21:27', 'crossval', 0.942857, 'breast-cancer-wisc', 'BaseRaF', 1, 0, '{}', 0.0166086, 0.0151967, 0.0017186, 28.89);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:21:40', 'crossval', 0.656438, 'breast-cancer', 'BaseRaF', 1, 0, '{}', 0.0500531, 0.0192996, 0.00201731, 64.88);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:25:14', 'crossval', 0.774788, 'cardiotocography-10clases', 'BaseRaF', 1, 0, '{}', 0.0179435, 0.42372, 0.111354, 255.01);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:27:36', 'crossval', 0.896715, 'cardiotocography-3clases', 'BaseRaF', 1, 0, '{}', 0.0129992, 0.381444, 0.0666812, 106.66);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:27:41', 'crossval', 0.772981, 'conn-bench-sonar-mines-rocks', 'BaseRaF', 1, 0, '{}', 0.054519, 0.00598101, 0.000759466, 13.5);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:02', 'crossval', 0.675117, 'cylinder-bands', 'BaseRaF', 1, 0, '{}', 0.0359021, 0.0249964, 0.00202589, 47.54);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:13', 'crossval', 0.970723, 'dermatology', 'BaseRaF', 1, 0, '{}', 0.0175846, 0.0121389, 0.0010901, 13.93);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:18', 'crossval', 0.753522, 'echocardiogram', 'BaseRaF', 1, 0, '{}', 0.0718413, 0.00750033, 0.00161811, 20.14);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:21', 'crossval', 0.798, 'fertility', 'BaseRaF', 1, 0, '{}', 0.0787592, 0.00438735, 0.00082982, 15.92);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:31', 'crossval', 0.720133, 'haberman-survival', 'BaseRaF', 1, 0, '{}', 0.0475346, 0.0131448, 0.00499674, 32.68);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:43', 'crossval', 0.779804, 'heart-hungarian', 'BaseRaF', 1, 0, '{}', 0.0445811, 0.0166187, 0.00157407, 50.97);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:28:47', 'crossval', 0.773671, 'hepatitis', 'BaseRaF', 1, 0, '{}', 0.0639841, 0.00578021, 0.000831726, 16.34);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:29:13', 'crossval', 0.696685, 'ilpd-indian-liver', 'BaseRaF', 1, 0, '{}', 0.0356957, 0.0304009, 0.010731, 78.23);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:29:22', 'crossval', 0.875389, 'ionosphere', 'BaseRaF', 1, 0, '{}', 0.0343367, 0.00810207, 0.00126368, 14.86);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:29:25', 'crossval', 0.953413, 'iris', 'BaseRaF', 1, 0, '{}', 0.0312751, 0.00319496, 0.000564433, 10.65);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:30:28', 'crossval', 0.70178, 'led-display', 'BaseRaF', 1, 0, '{}', 0.0236684, 0.0837334, 0.00515429, 133.27);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:31:24', 'crossval', 0.726722, 'libras', 'BaseRaF', 1, 0, '{}', 0.0462516, 0.194452, 0.0319483, 65);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:31:49', 'crossval', 0.790875, 'low-res-spect', 'BaseRaF', 1, 0, '{}', 0.0338948, 0.0700037, 0.00737621, 22.25);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:31:54', 'crossval', 0.761622, 'lymphography', 'BaseRaF', 1, 0, '{}', 0.072691, 0.00602972, 0.000858306, 17.09);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:32:50', 'crossval', 0.780206, 'mammographic', 'BaseRaF', 1, 0, '{}', 0.0261463, 0.0725665, 0.00665848, 177.33);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:32:52', 'crossval', 0.667239, 'molec-biol-promoter', 'BaseRaF', 1, 0, '{}', 0.0894839, 0.00204324, 0.000882715, 4.23);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:33:10', 'crossval', 0.834034, 'musk-1', 'BaseRaF', 1, 0, '{}', 0.0355503, 0.0294025, 0.00582493, 32.15);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:33:59', 'crossval', 0.792313, 'oocytes_merluccius_nucleus_4d', 'BaseRaF', 1, 0, '{}', 0.0236722, 0.0609631, 0.00810882, 76.76);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:34:47', 'crossval', 0.910551, 'oocytes_merluccius_states_2f', 'BaseRaF', 1, 0, '{}', 0.0178087, 0.0589824, 0.00537782, 46.61);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:35:27', 'crossval', 0.76193, 'oocytes_trisopterus_nucleus_2f', 'BaseRaF', 1, 0, '{}', 0.0290605, 0.0437252, 0.00599953, 84.37);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:36:01', 'crossval', 0.922149, 'oocytes_trisopterus_states_5b', 'BaseRaF', 1, 0, '{}', 0.016058, 0.0338147, 0.0038348, 28.44);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:36:06', 'crossval', 0.87924, 'parkinsons', 'BaseRaF', 1, 0, '{}', 0.0434764, 0.00664538, 0.000956323, 18.21);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:36:50', 'crossval', 0.697005, 'pima', 'BaseRaF', 1, 0, '{}', 0.0329339, 0.0598898, 0.00405292, 179.02);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:36:54', 'crossval', 0.81136, 'pittsburg-bridges-MATERIAL', 'BaseRaF', 1, 0, '{}', 0.0721078, 0.00614998, 0.000956048, 18.21);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:36:59', 'crossval', 0.622107, 'pittsburg-bridges-REL-L', 'BaseRaF', 1, 0, '{}', 0.0836926, 0.0107519, 0.00245178, 33.92);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:04', 'crossval', 0.630217, 'pittsburg-bridges-SPAN', 'BaseRaF', 1, 0, '{}', 0.0855243, 0.0103792, 0.00144035, 32.74);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:07', 'crossval', 0.821007, 'pittsburg-bridges-T-OR-D', 'BaseRaF', 1, 0, '{}', 0.071734, 0.00451751, 0.000865693, 15.6);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:16', 'crossval', 0.590586, 'planning', 'BaseRaF', 1, 0, '{}', 0.0720305, 0.0149104, 0.00202668, 49.52);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:21', 'crossval', 0.539375, 'post-operative', 'BaseRaF', 1, 0, '{}', 0.102817, 0.00860377, 0.00125772, 28.27);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:25', 'crossval', 0.942518, 'seeds', 'BaseRaF', 1, 0, '{}', 0.0293366, 0.00587637, 0.000679482, 14.71);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:37:27', 'crossval', 0.678261, 'statlog-australian-credit', 'BaseRaF', 1, 0, '{}', 0.0319213, 0.00851742, 0.00101665, 1);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:38:23', 'crossval', 0.68762, 'statlog-german-credit', 'BaseRaF', 1, 0, '{}', 0.0285632, 0.0759742, 0.00988248, 118.37);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:38:33', 'crossval', 0.747605, 'statlog-heart', 'BaseRaF', 1, 0, '{}', 0.0505201, 0.01431, 0.00186931, 34.9);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:41:32', 'crossval', 0.953604, 'statlog-image', 'BaseRaF', 1, 0, '{}', 0.0428999, 0.459961, 0.209446, 91.85);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:42:14', 'crossval', 0.789572, 'statlog-vehicle', 'BaseRaF', 1, 0, '{}', 0.0290904, 0.0418569, 0.00252999, 79.05);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:42:32', 'crossval', 0.971567, 'synthetic-control', 'BaseRaF', 1, 0, '{}', 0.0148618, 0.0233277, 0.00241783, 15.44);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:42:58', 'crossval', 0.974906, 'tic-tac-toe', 'BaseRaF', 1, 0, '{}', 0.0119599, 0.0372124, 0.00723869, 21.49);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:43:10', 'crossval', 0.822601, 'vertebral-column-2clases', 'BaseRaF', 1, 0, '{}', 0.0397492, 0.016375, 0.00309965, 45.54);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:43:13', 'crossval', 0.97748, 'wine', 'BaseRaF', 1, 0, '{}', 0.020518, 0.00259591, 0.000401448, 6.86);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std, nodes) values ('2021-04-10', '01:43:15', 'crossval', 0.936262, 'zoo', 'BaseRaF', 1, 0, '{}', 0.0494478, 0.00416789, 0.00034269, 13.2);

49
results/normalizados/cart.sql Normal file
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replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:19','crossval','0.7820799999999999','0.03608203985364464','balance-scale','cart','1','0','0.0006873464584350586','6.056292602707366e-05','{}','256.8','128.9','10.72');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:19','crossval','0.6833333333333335','0.26977356760397747','balloons','cart','1','0','0.0004589319229125977','6.682214388089997e-05','{}','11.36','6.18','3.46');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:20','crossval','0.9239217512808571','0.02323176886368342','breast-cancer-wisc-diag','cart','1','0','0.004066610336303711','0.00037735371598770866','{}','36.16','18.58','7.1');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:20','crossval','0.6919743589743588','0.07238575982092108','breast-cancer-wisc-prog','cart','1','0','0.0016518211364746093','0.00016523387709218434','{}','43.2','22.1','7.84');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:20','crossval','0.9434984583761563','0.02073055093670967','breast-cancer-wisc','cart','1','0','0.000748910903930664','6.367492730058185e-05','{}','57.52','29.26','8.72');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:20','crossval','0.6359225650332728','0.0534792000241946','breast-cancer','cart','1','0','0.0006718873977661133','8.342001699012386e-05','{}','164.72','82.86','15.56');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:20','crossval','0.8105368682684342','0.019247308169627633','cardiotocography-10clases','cart','1','0','0.009399790763854981','0.000310402623122502','{}','480.84','240.92','20.02');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:21','crossval','0.9203670809168737','0.01396855482026071','cardiotocography-3clases','cart','1','0','0.00814661979675293','0.0005860086312648286','{}','210.96','105.98','15.88');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:21','crossval','0.7258768873403021','0.0682090752129407','conn-bench-sonar-mines-rocks','cart','1','0','0.0025511789321899414','0.0002510580336619537','{}','34.16','17.58','6.44');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:21','crossval','0.7130211307824103','0.04627911197423834','cylinder-bands','cart','1','0','0.0021189451217651367','0.000164014995951283','{}','131.96','66.48','13.46');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:21','crossval','0.9396038504257683','0.02387579095888003','dermatology','cart','1','0','0.0007885885238647461','0.00011540453341909482','{}','34.6','17.8','10.82');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:21','crossval','0.7458404558404559','0.080776687074494','echocardiogram','cart','1','0','0.0005904006958007813','6.0040661402099946e-05','{}','32.6','16.8','8.82');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:21','crossval','0.7989999999999999','0.08395832299420945','fertility','cart','1','0','0.0004935407638549805','7.218480550167721e-05','{}','28.84','14.92','7.16');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:21','crossval','0.6382178741406662','0.05575144578993558','haberman-survival','cart','1','0','0.0006317853927612305','7.359752923339246e-05','{}','160.92','80.96','15.1');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:21','crossval','0.7502746931618937','0.044645868130612026','heart-hungarian','cart','1','0','0.0007543468475341797','8.493027944498374e-05','{}','83.08','42.04','9.6');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:22','crossval','0.7658064516129032','0.07350593002050229','hepatitis','cart','1','0','0.0006294441223144531','7.136307121991442e-05','{}','37.0','19.0','7.66');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:22','crossval','0.6625906277630415','0.03822153903302792','ilpd-indian-liver','cart','1','0','0.0015907526016235352','0.00010613849153297268','{}','186.32','93.66','18.08');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:22','crossval','0.8757907444668007','0.03797437861573896','ionosphere','cart','1','0','0.0031133270263671874','0.0004496306989895815','{}','40.32','20.66','9.88');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:22','crossval','0.94','0.043716256828679995','iris','cart','1','0','0.0004883623123168946','6.707633932216357e-05','{}','15.08','8.04','5.04');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:22','crossval','0.7037','0.029644729717101474','led-display','cart','1','0','0.0006838130950927734','7.497018504393881e-05','{}','156.6','78.8','7.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:22','crossval','0.6547222222222221','0.05983580929572253','libras','cart','1','0','0.00954397201538086','0.00041554126865619165','{}','132.32','66.66','11.16');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:23','crossval','0.8352036677834599','0.030503013555880365','low-res-spect','cart','1','0','0.01601393222808838','0.001041629752684619','{}','74.96','37.98','8.74');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:23','crossval','0.7690114942528736','0.0755835919787076','lymphography','cart','1','0','0.0005543041229248047','7.206299803216294e-05','{}','49.0','25.0','7.86');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:23','crossval','0.7557739637305698','0.02089031159057769','mammographic','cart','1','0','0.0009842538833618165','8.02737307480172e-05','{}','403.12','202.06','20.46');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:24','crossval','0.7158008658008658','0.08811757165025375','molec-biol-promoter','cart','1','0','0.0007098007202148437','9.733050797678472e-05','{}','22.24','11.62','5.24');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:25','crossval','0.777530701754386','0.04335321566037286','musk-1','cart','1','0','0.018938016891479493','0.0023793265663644494','{}','75.12','38.06','12.5');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:25','crossval','0.7196642754662842','0.03071950171876295','oocytes_merluccius_nucleus_4d','cart','1','0','0.013992009162902832','0.0011456969136993383','{}','222.24','111.62','15.08');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:26','crossval','0.8911927307508368','0.02477313298773685','oocytes_merluccius_states_2f','cart','1','0','0.007075467109680176','0.000580046153013565','{}','98.84','49.92','10.78');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:26','crossval','0.7257617246141836','0.029837690053299604','oocytes_trisopterus_nucleus_2f','cart','1','0','0.008345584869384765','0.00042809521443671906','{}','192.76','96.88','15.64');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:27','crossval','0.8702630156728517','0.025916263242401694','oocytes_trisopterus_states_5b','cart','1','0','0.009247007369995118','0.0004790588538986512','{}','109.12','55.06','12.56');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:27','crossval','0.8558974358974359','0.05822037527622443','parkinsons','cart','1','0','0.0010352849960327149','9.855723737448487e-05','{}','24.84','12.92','5.54');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:27','crossval','0.7009116373822256','0.031101392763464096','pima','cart','1','0','0.001743307113647461','0.00010421535409735057','{}','218.52','109.76','14.28');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:27','crossval','0.8006493506493506','0.07641918889128255','pittsburg-bridges-MATERIAL','cart','1','0','0.0004951238632202148','6.908799856069978e-05','{}','33.24','17.12','7.18');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:27','crossval','0.6143333333333333','0.09787364013413712','pittsburg-bridges-REL-L','cart','1','0','0.0005135631561279297','6.495038402721139e-05','{}','57.72','29.36','12.18');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:27','crossval','0.5564912280701755','0.09603550008778475','pittsburg-bridges-SPAN','cart','1','0','0.0005118465423583985','7.031496030423733e-05','{}','55.36','28.18','10.1');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:27','crossval','0.8234761904761905','0.08723519697826523','pittsburg-bridges-T-OR-D','cart','1','0','0.0004817628860473633','7.016788984381056e-05','{}','26.44','13.72','5.94');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:27','crossval','0.573963963963964','0.07834935548163284','planning','cart','1','0','0.0010381507873535156','0.00011806603047514821','{}','60.56','30.78','12.18');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:27','crossval','0.5822222222222222','0.10554970744033704','post-operative','cart','1','0','0.0005062770843505859','7.122806409227687e-05','{}','66.32','33.66','11.24');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:27','crossval','0.9157142857142857','0.042380952380952366','seeds','cart','1','0','0.0006688928604125976','6.788828620597182e-05','{}','25.04','13.02','6.14');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:27','crossval','0.572463768115942','0.040476058097881','statlog-australian-credit','cart','1','0','0.0019480609893798829','0.00011803304739191946','{}','269.88','135.44','18.46');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:28','crossval','0.6899','0.025129464777428084','statlog-german-credit','cart','1','0','0.0021873950958251954','9.371859211329562e-05','{}','331.92','166.46','16.42');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:28','crossval','0.7351851851851852','0.045699861775011355','statlog-heart','cart','1','0','0.0007651948928833008','6.677668508558311e-05','{}','73.68','37.34','8.86');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:28','crossval','0.9616017316017317','0.009472609323341975','statlog-image','cart','1','0','0.008970613479614259','0.0003288094001180248','{}','127.32','64.16','15.94');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:28','crossval','0.7063849634528365','0.030574747774242836','statlog-vehicle','cart','1','0','0.003118605613708496','0.00014468487911691033','{}','233.52','117.26','16.26');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:29','crossval','0.9023333333333332','0.02511307760244982','synthetic-control','cart','1','0','0.00933086395263672','0.00039118840253255936','{}','52.16','26.58','8.58');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:29','crossval','0.9522851221640489','0.018529859619663462','tic-tac-toe','cart','1','0','0.0008065223693847657','9.298620463823368e-05','{}','119.2','60.1','9.28');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:29','crossval','0.7996774193548387','0.04231712199258938','vertebral-column-2clases','cart','1','0','0.0008004426956176758','9.684473759962773e-05','{}','61.16','31.08','9.3');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:29','crossval','0.9015873015873015','0.0526003102936011','wine','cart','1','0','0.0007057809829711915','6.556413493732969e-05','{}','16.24','8.62','4.38');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:41:29','crossval','0.9555714285714286','0.044427856881917915','zoo','cart','1','0','0.0004845094680786133','6.487745318311946e-05','{}','17.96','9.48','6.8');

55
results/normalizados/cart.txt Normal file
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* Process all datasets set with cart: tanveer norm: True std: False store in: cart
5 Fold Cross Validation with 10 random seeds [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
Dataset Samp Var Cls Nodes Leaves Depth Accuracy Time Parameters
============================== ===== === === ======= ======= ======= =============== =============== ==========
balance-scale 625 4 3 256.80 128.90 10.72 0.782080±0.0361 0.000687±0.0001 {}
balloons 16 4 2 11.36 6.18 3.46 0.683333±0.2698 0.000459±0.0001 {}
breast-cancer-wisc-diag 569 30 2 36.16 18.58 7.10 0.923922±0.0232 0.004067±0.0004 {}
breast-cancer-wisc-prog 198 33 2 43.20 22.10 7.84 0.691974±0.0724 0.001652±0.0002 {}
breast-cancer-wisc 699 9 2 57.52 29.26 8.72 0.943498±0.0207 0.000749±0.0001 {}
breast-cancer 286 9 2 164.72 82.86 15.56 0.635923±0.0535 0.000672±0.0001 {}
cardiotocography-10clases 2126 21 10 480.84 240.92 20.02 0.810537±0.0192 0.009400±0.0003 {}
cardiotocography-3clases 2126 21 3 210.96 105.98 15.88 0.920367±0.0140 0.008147±0.0006 {}
conn-bench-sonar-mines-rocks 208 60 2 34.16 17.58 6.44 0.725877±0.0682 0.002551±0.0003 {}
cylinder-bands 512 35 2 131.96 66.48 13.46 0.713021±0.0463 0.002119±0.0002 {}
dermatology 366 34 6 34.60 17.80 10.82 0.939604±0.0239 0.000789±0.0001 {}
echocardiogram 131 10 2 32.60 16.80 8.82 0.745840±0.0808 0.000590±0.0001 {}
fertility 100 9 2 28.84 14.92 7.16 0.799000±0.0840 0.000494±0.0001 {}
haberman-survival 306 3 2 160.92 80.96 15.10 0.638218±0.0558 0.000632±0.0001 {}
heart-hungarian 294 12 2 83.08 42.04 9.60 0.750275±0.0446 0.000754±0.0001 {}
hepatitis 155 19 2 37.00 19.00 7.66 0.765806±0.0735 0.000629±0.0001 {}
ilpd-indian-liver 583 9 2 186.32 93.66 18.08 0.662591±0.0382 0.001591±0.0001 {}
ionosphere 351 33 2 40.32 20.66 9.88 0.875791±0.0380 0.003113±0.0004 {}
iris 150 4 3 15.08 8.04 5.04 0.940000±0.0437 0.000488±0.0001 {}
led-display 1000 7 10 156.60 78.80 7.00 0.703700±0.0296 0.000684±0.0001 {}
libras 360 90 15 132.32 66.66 11.16 0.654722±0.0598 0.009544±0.0004 {}
low-res-spect 531 100 9 74.96 37.98 8.74 0.835204±0.0305 0.016014±0.0010 {}
lymphography 148 18 4 49.00 25.00 7.86 0.769011±0.0756 0.000554±0.0001 {}
mammographic 961 5 2 403.12 202.06 20.46 0.755774±0.0209 0.000984±0.0001 {}
molec-biol-promoter 106 57 2 22.24 11.62 5.24 0.715801±0.0881 0.000710±0.0001 {}
musk-1 476 166 2 75.12 38.06 12.50 0.777531±0.0434 0.018938±0.0024 {}
oocytes_merluccius_nucleus_4d 1022 41 2 222.24 111.62 15.08 0.719664±0.0307 0.013992±0.0011 {}
oocytes_merluccius_states_2f 1022 25 3 98.84 49.92 10.78 0.891193±0.0248 0.007075±0.0006 {}
oocytes_trisopterus_nucleus_2f 912 25 2 192.76 96.88 15.64 0.725762±0.0298 0.008346±0.0004 {}
oocytes_trisopterus_states_5b 912 32 3 109.12 55.06 12.56 0.870263±0.0259 0.009247±0.0005 {}
parkinsons 195 22 2 24.84 12.92 5.54 0.855897±0.0582 0.001035±0.0001 {}
pima 768 8 2 218.52 109.76 14.28 0.700912±0.0311 0.001743±0.0001 {}
pittsburg-bridges-MATERIAL 106 7 3 33.24 17.12 7.18 0.800649±0.0764 0.000495±0.0001 {}
pittsburg-bridges-REL-L 103 7 3 57.72 29.36 12.18 0.614333±0.0979 0.000514±0.0001 {}
pittsburg-bridges-SPAN 92 7 3 55.36 28.18 10.10 0.556491±0.0960 0.000512±0.0001 {}
pittsburg-bridges-T-OR-D 102 7 2 26.44 13.72 5.94 0.823476±0.0872 0.000482±0.0001 {}
planning 182 12 2 60.56 30.78 12.18 0.573964±0.0783 0.001038±0.0001 {}
post-operative 90 8 3 66.32 33.66 11.24 0.582222±0.1055 0.000506±0.0001 {}
seeds 210 7 3 25.04 13.02 6.14 0.915714±0.0424 0.000669±0.0001 {}
statlog-australian-credit 690 14 2 269.88 135.44 18.46 0.572464±0.0405 0.001948±0.0001 {}
statlog-german-credit 1000 24 2 331.92 166.46 16.42 0.689900±0.0251 0.002187±0.0001 {}
statlog-heart 270 13 2 73.68 37.34 8.86 0.735185±0.0457 0.000765±0.0001 {}
statlog-image 2310 18 7 127.32 64.16 15.94 0.961602±0.0095 0.008971±0.0003 {}
statlog-vehicle 846 18 4 233.52 117.26 16.26 0.706385±0.0306 0.003119±0.0001 {}
synthetic-control 600 60 6 52.16 26.58 8.58 0.902333±0.0251 0.009331±0.0004 {}
tic-tac-toe 958 9 2 119.20 60.10 9.28 0.952285±0.0185 0.000807±0.0001 {}
vertebral-column-2clases 310 6 2 61.16 31.08 9.30 0.799677±0.0423 0.000800±0.0001 {}
wine 178 13 3 16.24 8.62 4.38 0.901587±0.0526 0.000706±0.0001 {}
zoo 101 16 7 17.96 9.48 6.80 0.955571±0.0444 0.000485±0.0001 {}
Time: 0h 0m 9s

49
results/normalizados/j48svm.sql Normal file
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insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'balance-scale', 0.94, 0.021836, 0.14862, 0.054757, 35.6);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'balloons', 0.595, 0.187831, 0.00504, 0.002683, 4.4);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'breast-cancer-wisc-diag', 0.952878, 0.023749, 0.03688, 0.007506, 13.48);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'breast-cancer-wisc-prog', 0.724038, 0.072215, 0.03234, 0.006895, 21.52);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'breast-cancer-wisc', 0.967674, 0.012478, 0.04816, 0.006961, 8.28);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'breast-cancer', 0.707719, 0.052367, 0.0713, 0.010275, 23.16);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'cardiotocography-10clases', 0.830812, 0.016194, 1.42148, 0.089646, 197.6);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'cardiotocography-3clases', 0.927327, 0.013055, 0.40532, 0.018162, 92.24);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'conn-bench-sonar-mines-rocks', 0.73892, 0.06386, 0.04418, 0.005117, 25.2);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'cylinder-bands', 0.726351, 0.029474, 0.1099, 0.0079, 85.4);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'dermatology', 0.955735, 0.023581, 0.0738, 0.016643, 15.12);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'echocardiogram', 0.805527, 0.084433, 0.02296, 0.005407, 13.4);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'fertility', 0.857, 0.071421, 0.02498, 0.005998, 4.48);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'haberman-survival', 0.714056, 0.04944, 0.02248, 0.010216, 4.44);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'heart-hungarian', 0.785026, 0.048182, 0.06344, 0.006888, 22.12);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'hepatitis', 0.761935, 0.058025, 0.03602, 0.005464, 16.6);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'ilpd-indian-liver', 0.690339, 0.04248, 0.05586, 0.011349, 14.24);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'ionosphere', 0.891984, 0.033857, 0.0462, 0.0036, 23.64);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'iris', 0.947333, 0.035957, 0.0133, 0.002914, 7.04);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'led-display', 0.7204, 0.028193, 1.31958, 0.102793, 13.04);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'libras', 0.66, 0.062869, 1.09746, 0.078557, 88.24);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'low-res-spect', 0.83358, 0.038622, 0.34854, 0.027053, 45.8);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'lymphography', 0.778552, 0.078755, 0.0553, 0.009578, 25.28);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'mammographic', 0.821435, 0.024479, 0.09182, 0.011567, 18.68);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'molec-biol-promoter', 0.744935, 0.092023, 0.02698, 0.004701, 16.0);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'musk-1', 0.82693, 0.046843, 0.32902, 0.029412, 50.72);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'oocytes_merluccius_nucleus_4d', 0.741766, 0.034628, 0.23738, 0.025296, 78.04);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'oocytes_merluccius_states_2f', 0.901374, 0.021463, 0.12582, 0.017535, 38.36);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'oocytes_trisopterus_nucleus_2f', 0.756587, 0.034538, 0.2026, 0.047357, 57.56);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'oocytes_trisopterus_states_5b', 0.887943, 0.02368, 0.1566, 0.015336, 56.12);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'parkinsons', 0.844615, 0.051642, 0.02474, 0.004582, 18.04);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'pima', 0.749876, 0.026553, 0.06754, 0.012694, 22.68);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'pittsburg-bridges-MATERIAL', 0.855844, 0.070512, 0.04744, 0.009731, 7.12);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'pittsburg-bridges-REL-L', 0.645048, 0.103969, 0.0741, 0.013543, 13.52);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'pittsburg-bridges-SPAN', 0.621579, 0.123278, 0.05116, 0.010828, 10.7);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'pittsburg-bridges-T-OR-D', 0.838333, 0.077644, 0.02828, 0.00596, 6.72);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'planning', 0.711381, 0.050453, 0.0074, 0.003504, 1.08);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'post-operative', 0.701111, 0.086702, 0.0495, 0.013578, 1.88);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'seeds', 0.909524, 0.049027, 0.02798, 0.006433, 13.28);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'statlog-australian-credit', 0.66029, 0.03596, 0.17408, 0.022439, 85.16);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'statlog-german-credit', 0.7244, 0.028664, 0.23886, 0.01523, 127.24);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'statlog-heart', 0.795926, 0.049896, 0.0645, 0.007953, 18.64);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'statlog-image', 0.967403, 0.010826, 0.35438, 0.03814, 69.8);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'statlog-vehicle', 0.729651, 0.03218, 0.29036, 0.024589, 93.44);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'synthetic-control', 0.922333, 0.030342, 0.17098, 0.019401, 35.32);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'tic-tac-toe', 0.983295, 0.007889, 0.07022, 0.007588, 3.0);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'vertebral-column-2clases', 0.84871, 0.047834, 0.02314, 0.006512, 10.32);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'wine', 0.979143, 0.020263, 0.00792, 0.002115, 4.32);
insert into results(norm, date, time, type, classifier, dataset, accuracy, accuracy_std, time_spent, time_spent_std, nodes) values(1, '2021-04-09', '23:43:20', 'crossval', 'j48svm', 'zoo', 0.92381, 0.046715, 0.09676, 0.012512, 14.48);

49
results/normalizados/stree.sql Normal file
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@@ -0,0 +1,49 @@
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:02','crossval','0.9918399999999999','0.010791403986507048','balance-scale','stree','1','0','0.01564922332763672','0.001228058544548453','{"C": 10000.0, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}','7.0','4.0','3.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:02','crossval','0.6066666666666667','0.30688398097290415','balloons','stree','1','0','0.0009676647186279296','0.0002367881119400382','{"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}','3.04','2.02','2.02');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:02','crossval','0.9481586710138177','0.017762307552983747','breast-cancer-wisc-diag','stree','1','0','0.002857346534729004','0.00019842965791362592','{"C": 0.2, "max_iter": 10000.0}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:03','crossval','0.7657051282051281','0.06922023305390425','breast-cancer-wisc-prog','stree','1','0','0.002074685096740723','0.0004066224889716958','{"C": 0.2, "max_iter": 10000.0}','2.56','1.78','1.78');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:03','crossval','0.9656628982528263','0.012882720775961002','breast-cancer-wisc','stree','1','0','0.0034239959716796877','0.0004755211146145311','{}','3.92','2.46','2.46');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:04','crossval','0.7255051421657592','0.04596424345908255','breast-cancer','stree','1','0','0.006020359992980957','0.001990502673664083','{}','9.52','5.26','4.04');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:17','crossval','0.6029189726594865','0.023230292245275567','cardiotocography-10clases','stree','1','0','0.25811831951141356','0.0314210294333643','{}','30.08','15.54','12.12');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:19','crossval','0.8842913007456503','0.01580017229575517','cardiotocography-3clases','stree','1','0','0.032402758598327634','0.0029452135541565977','{}','9.52','5.26','5.12');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:19','crossval','0.771602787456446','0.0587094228135243','conn-bench-sonar-mines-rocks','stree','1','0','0.008197841644287109','0.0014311584492272215','{}','5.44','3.22','2.84');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:20','crossval','0.6843613173424711','0.0403270755872992','cylinder-bands','stree','1','0','0.019214744567871093','0.0036259513257220024','{}','3.52','2.26','2.24');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:26','crossval','0.9652832284339133','0.01840681935790365','dermatology','stree','1','0','0.10673258781433105','0.011071385618113641','{"C": 55, "max_iter": 10000.0}','11.4','6.2','6.2');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:26','crossval','0.6716239316239316','0.08877112514663672','echocardiogram','stree','1','0','0.0010528135299682616','0.00012329582272334363','{"C": 7, "gamma": 0.1, "kernel": "poly", "max_features": "auto", "max_iter": 10000.0}','1.0','1.0','1.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:26','crossval','0.88','0.0547722557505166','fertility','stree','1','0','0.0009029483795166015','8.985260287819814e-05','{"C": 0.05, "max_features": "auto", "max_iter": 10000.0}','1.0','1.0','1.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:27','crossval','0.7336805922792174','0.049766281568050116','haberman-survival','stree','1','0','0.004347615242004395','0.0008138944455178978','{}','6.48','3.74','3.28');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:27','crossval','0.7075043834015196','0.06988170032375486','heart-hungarian','stree','1','0','0.002285408973693848','0.00018612982992200836','{"C": 0.05, "max_iter": 10000.0}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:27','crossval','0.7877419354838711','0.07215718764924595','hepatitis','stree','1','0','0.002471780776977539','0.0005861380007886405','{"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}','2.48','1.74','1.74');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:27','crossval','0.7130518715001475','0.03723387507571413','ilpd-indian-liver','stree','1','0','0.0021682167053222656','0.0009278573484369175','{}','1.88','1.44','1.42');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:28','crossval','0.9302092555331992','0.0315040432760966','ionosphere','stree','1','0','0.007402029037475586','0.0007914535520963372','{"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}','3.2','2.1','2.1');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:28','crossval','0.9520000000000001','0.029933259094191526','iris','stree','1','0','0.0032801103591918947','0.0001681739375532264','{}','5.0','3.0','3.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:35','crossval','0.6995999999999999','0.02761955828756136','led-display','stree','1','0','0.1255587148666382','0.005677196093766789','{}','34.76','17.88','13.3');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:48','crossval','0.2536111111111111','0.03786448383119027','libras','stree','1','0','0.2506489419937134','0.018599137274566482','{"C": 0.08, "max_iter": 10000.0}','19.32','10.16','9.94');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:50','crossval','0.6894515958384763','0.043401193625496784','low-res-spect','stree','1','0','0.03629184246063232','0.0026942266748541423','{"C": 0.05, "max_iter": 10000.0}','9.52','5.26','5.24');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:50','crossval','0.759977011494253','0.09836735406434544','lymphography','stree','1','0','0.002750692367553711','0.0003574147020189625','{"C": 0.05, "max_iter": 10000.0}','3.36','2.18','2.18');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:51','crossval','0.805308182210708','0.024897168922554405','mammographic','stree','1','0','0.003717927932739258','0.0002334562471656256','{}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:51','crossval','0.7369264069264068','0.10707633598969517','molec-biol-promoter','stree','1','0','0.0019171428680419922','0.0002558217748088511','{"C": 0.05, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:52','crossval','0.565094298245614','0.04088778951436713','musk-1','stree','1','0','0.012906560897827149','0.0003487001831039861','{"C": 0.05, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}','1.0','1.0','1.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:54','crossval','0.6696681013868963','0.02466397638475715','oocytes_merluccius_nucleus_4d','stree','1','0','0.036813783645629886','0.011420072913938121','{"C": 8.25, "gamma": 0.1, "kernel": "poly"}','2.16','1.58','1.58');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:56','crossval','0.9060664753706361','0.01947618443632987','oocytes_merluccius_states_2f','stree','1','0','0.03827941417694092','0.0022696202549713908','{}','6.04','3.52','3.52');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:57','crossval','0.7188584639404313','0.03644014184644201','oocytes_trisopterus_nucleus_2f','stree','1','0','0.017165846824645996','0.0031658347443004943','{}','3.6','2.3','2.24');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:58','crossval','0.8030703176604815','0.023544698695572304','oocytes_trisopterus_states_5b','stree','1','0','0.011374692916870117','0.0002904977551424975','{"C": 0.11, "max_iter": 10000.0}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:58','crossval','0.8543589743589743','0.04957147066090082','parkinsons','stree','1','0','0.0029172849655151366','0.000254502288407107','{}','3.04','2.02','2.02');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:58','crossval','0.7688659706306764','0.030512242922947463','pima','stree','1','0','0.004713902473449707','0.00036234890230219815','{}','3.16','2.08','2.08');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:59','crossval','0.7584848484848484','0.0954131508049414','pittsburg-bridges-MATERIAL','stree','1','0','0.002017765045166016','0.0007032473277629264','{"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}','2.28','1.64','1.64');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:59','crossval','0.6221904761904762','0.09159389814212923','pittsburg-bridges-REL-L','stree','1','0','0.005073995590209961','0.0015471340861317874','{}','6.24','3.62','3.5');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:32:59','crossval','0.5885964912280701','0.13116275959717713','pittsburg-bridges-SPAN','stree','1','0','0.0027930879592895507','0.0005646042707767776','{"C": 0.05, "max_iter": 10000.0}','4.08','2.54','2.54');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:33:00','crossval','0.8638095238095238','0.07623659318588133','pittsburg-bridges-T-OR-D','stree','1','0','0.0016234731674194336','0.00037310714376946114','{}','2.48','1.74','1.74');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:33:00','crossval','0.6332582582582582','0.07931272186851575','planning','stree','1','0','0.003254857063293457','0.00034353820348752235','{"C": 7, "gamma": 10.0, "kernel": "rbf", "max_iter": 10000.0}','4.24','2.62','2.62');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:33:00','crossval','0.711111111111111','0.07535922203472521','post-operative','stree','1','0','0.0011165428161621093','8.337944115351184e-05','{"C": 55, "degree": 5, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}','1.0','1.0','1.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:33:02','crossval','0.9499999999999998','0.03816957033781285','seeds','stree','1','0','0.03826602935791015','0.00878652443250852','{"C": 10000.0, "max_iter": 10000.0}','11.2','6.1','4.84');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:33:02','crossval','0.6782608695652174','0.03904983647915211','statlog-australian-credit','stree','1','0','0.0011624908447265625','0.00012125793010232072','{"C": 0.05, "max_features": "auto", "max_iter": 10000.0}','1.0','1.0','1.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:33:04','crossval','0.7695000000000001','0.027771388153997628','statlog-german-credit','stree','1','0','0.017214603424072265','0.00353252091192701','{}','8.64','4.82','3.66');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:33:04','crossval','0.8425925925925926','0.039064857610609224','statlog-heart','stree','1','0','0.003080282211303711','8.908118032360401e-05','{}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:33:18','crossval','0.9134632034632034','0.014399975949955872','statlog-image','stree','1','0','0.28190260887145996','0.011044599418803204','{"C": 7, "max_iter": 10000.0}','17.76','9.38','8.44');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:33:20','crossval','0.7022721893491126','0.033859340958035986','statlog-vehicle','stree','1','0','0.03189016342163086','0.0035088938107595263','{}','13.36','7.18','5.66');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:33:27','crossval','0.9469999999999998','0.019983326383095815','synthetic-control','stree','1','0','0.13779988765716553','0.006975647611245406','{"C": 0.55, "max_iter": 10000.0}','11.28','6.14','6.14');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:33:28','crossval','0.6534505890052357','0.028021260679892277','tic-tac-toe','stree','1','0','0.013150224685668946','0.00026458604250187765','{"C": 0.2, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}','1.0','1.0','1.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:33:28','crossval','0.8074193548387096','0.05185527767837077','vertebral-column-2clases','stree','1','0','0.0025788545608520508','0.00010979391980186945','{}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:33:29','crossval','0.9685555555555557','0.030594137669873376','wine','stree','1','0','0.0034023189544677736','0.00033841067192203703','{"C": 0.55, "max_iter": 10000.0}','5.0','3.0','3.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:33:29','crossval','0.877','0.06800461835723977','zoo','stree','1','0','0.005529937744140625','0.0006721646227264098','{"C": 0.1, "max_iter": 10000.0}','12.12','6.56','6.56');

55
results/normalizados/stree.txt Normal file
View File

@@ -0,0 +1,55 @@
* Process all datasets set with stree: tanveer norm: True std: False store in: stree
5 Fold Cross Validation with 10 random seeds [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
Dataset Samp Var Cls Nodes Leaves Depth Accuracy Time Parameters
============================== ===== === === ======= ======= ======= =============== =============== ==========
balance-scale 625 4 3 7.00 4.00 3.00 0.991840±0.0108 0.015649±0.0012 {"C": 10000.0, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}
balloons 16 4 2 3.04 2.02 2.02 0.606667±0.3069 0.000968±0.0002 {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}
breast-cancer-wisc-diag 569 30 2 3.00 2.00 2.00 0.948159±0.0178 0.002857±0.0002 {"C": 0.2, "max_iter": 10000.0}
breast-cancer-wisc-prog 198 33 2 2.56 1.78 1.78 0.765705±0.0692 0.002075±0.0004 {"C": 0.2, "max_iter": 10000.0}
breast-cancer-wisc 699 9 2 3.92 2.46 2.46 0.965663±0.0129 0.003424±0.0005 {}
breast-cancer 286 9 2 9.52 5.26 4.04 0.725505±0.0460 0.006020±0.0020 {}
cardiotocography-10clases 2126 21 10 30.08 15.54 12.12 0.602919±0.0232 0.258118±0.0314 {}
cardiotocography-3clases 2126 21 3 9.52 5.26 5.12 0.884291±0.0158 0.032403±0.0029 {}
conn-bench-sonar-mines-rocks 208 60 2 5.44 3.22 2.84 0.771603±0.0587 0.008198±0.0014 {}
cylinder-bands 512 35 2 3.52 2.26 2.24 0.684361±0.0403 0.019215±0.0036 {}
dermatology 366 34 6 11.40 6.20 6.20 0.965283±0.0184 0.106733±0.0111 {"C": 55, "max_iter": 10000.0}
echocardiogram 131 10 2 1.00 1.00 1.00 0.671624±0.0888 0.001053±0.0001 {"C": 7, "gamma": 0.1, "kernel": "poly", "max_features": "auto", "max_iter": 10000.0}
fertility 100 9 2 1.00 1.00 1.00 0.880000±0.0548 0.000903±0.0001 {"C": 0.05, "max_features": "auto", "max_iter": 10000.0}
haberman-survival 306 3 2 6.48 3.74 3.28 0.733681±0.0498 0.004348±0.0008 {}
heart-hungarian 294 12 2 3.00 2.00 2.00 0.707504±0.0699 0.002285±0.0002 {"C": 0.05, "max_iter": 10000.0}
hepatitis 155 19 2 2.48 1.74 1.74 0.787742±0.0722 0.002472±0.0006 {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}
ilpd-indian-liver 583 9 2 1.88 1.44 1.42 0.713052±0.0372 0.002168±0.0009 {}
ionosphere 351 33 2 3.20 2.10 2.10 0.930209±0.0315 0.007402±0.0008 {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}
iris 150 4 3 5.00 3.00 3.00 0.952000±0.0299 0.003280±0.0002 {}
led-display 1000 7 10 34.76 17.88 13.30 0.699600±0.0276 0.125559±0.0057 {}
libras 360 90 15 19.32 10.16 9.94 0.253611±0.0379 0.250649±0.0186 {"C": 0.08, "max_iter": 10000.0}
low-res-spect 531 100 9 9.52 5.26 5.24 0.689452±0.0434 0.036292±0.0027 {"C": 0.05, "max_iter": 10000.0}
lymphography 148 18 4 3.36 2.18 2.18 0.759977±0.0984 0.002751±0.0004 {"C": 0.05, "max_iter": 10000.0}
mammographic 961 5 2 3.00 2.00 2.00 0.805308±0.0249 0.003718±0.0002 {}
molec-biol-promoter 106 57 2 3.00 2.00 2.00 0.736926±0.1071 0.001917±0.0003 {"C": 0.05, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}
musk-1 476 166 2 1.00 1.00 1.00 0.565094±0.0409 0.012907±0.0003 {"C": 0.05, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}
oocytes_merluccius_nucleus_4d 1022 41 2 2.16 1.58 1.58 0.669668±0.0247 0.036814±0.0114 {"C": 8.25, "gamma": 0.1, "kernel": "poly"}
oocytes_merluccius_states_2f 1022 25 3 6.04 3.52 3.52 0.906066±0.0195 0.038279±0.0023 {}
oocytes_trisopterus_nucleus_2f 912 25 2 3.60 2.30 2.24 0.718858±0.0364 0.017166±0.0032 {}
oocytes_trisopterus_states_5b 912 32 3 3.00 2.00 2.00 0.803070±0.0235 0.011375±0.0003 {"C": 0.11, "max_iter": 10000.0}
parkinsons 195 22 2 3.04 2.02 2.02 0.854359±0.0496 0.002917±0.0003 {}
pima 768 8 2 3.16 2.08 2.08 0.768866±0.0305 0.004714±0.0004 {}
pittsburg-bridges-MATERIAL 106 7 3 2.28 1.64 1.64 0.758485±0.0954 0.002018±0.0007 {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}
pittsburg-bridges-REL-L 103 7 3 6.24 3.62 3.50 0.622190±0.0916 0.005074±0.0015 {}
pittsburg-bridges-SPAN 92 7 3 4.08 2.54 2.54 0.588596±0.1312 0.002793±0.0006 {"C": 0.05, "max_iter": 10000.0}
pittsburg-bridges-T-OR-D 102 7 2 2.48 1.74 1.74 0.863810±0.0762 0.001623±0.0004 {}
planning 182 12 2 4.24 2.62 2.62 0.633258±0.0793 0.003255±0.0003 {"C": 7, "gamma": 10.0, "kernel": "rbf", "max_iter": 10000.0}
post-operative 90 8 3 1.00 1.00 1.00 0.711111±0.0754 0.001117±0.0001 {"C": 55, "degree": 5, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}
seeds 210 7 3 11.20 6.10 4.84 0.950000±0.0382 0.038266±0.0088 {"C": 10000.0, "max_iter": 10000.0}
statlog-australian-credit 690 14 2 1.00 1.00 1.00 0.678261±0.0390 0.001162±0.0001 {"C": 0.05, "max_features": "auto", "max_iter": 10000.0}
statlog-german-credit 1000 24 2 8.64 4.82 3.66 0.769500±0.0278 0.017215±0.0035 {}
statlog-heart 270 13 2 3.00 2.00 2.00 0.842593±0.0391 0.003080±0.0001 {}
statlog-image 2310 18 7 17.76 9.38 8.44 0.913463±0.0144 0.281903±0.0110 {"C": 7, "max_iter": 10000.0}
statlog-vehicle 846 18 4 13.36 7.18 5.66 0.702272±0.0339 0.031890±0.0035 {}
synthetic-control 600 60 6 11.28 6.14 6.14 0.947000±0.0200 0.137800±0.0070 {"C": 0.55, "max_iter": 10000.0}
tic-tac-toe 958 9 2 1.00 1.00 1.00 0.653451±0.0280 0.013150±0.0003 {"C": 0.2, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}
vertebral-column-2clases 310 6 2 3.00 2.00 2.00 0.807419±0.0519 0.002579±0.0001 {}
wine 178 13 3 5.00 3.00 3.00 0.968556±0.0306 0.003402±0.0003 {"C": 0.55, "max_iter": 10000.0}
zoo 101 16 7 12.12 6.56 6.56 0.877000±0.0680 0.005530±0.0007 {"C": 0.1, "max_iter": 10000.0}
Time: 0h 1m 28s

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replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:34:48','crossval','0.9078400000000001','0.027022479530938694','balance-scale','stree_default','1','0','0.014065136909484863','0.003356256194885882','{}','16.32','8.66','6.68');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:34:48','crossval','0.6416666666666666','0.27600825269465323','balloons','stree_default','1','0','0.0014453935623168945','0.00048784805125499193','{}','4.08','2.54','2.34');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:34:49','crossval','0.9666092221704703','0.016581312614548083','breast-cancer-wisc-diag','stree_default','1','0','0.004474539756774903','0.0005162784948413271','{}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:34:49','crossval','0.7908461538461538','0.06633460694762172','breast-cancer-wisc-prog','stree_default','1','0','0.004705686569213868','0.0005874887138947772','{}','3.44','2.22','2.22');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:34:49','crossval','0.9656628982528263','0.012882720775961002','breast-cancer-wisc','stree_default','1','0','0.003395237922668457','0.0004947719857876927','{}','3.92','2.46','2.46');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:34:49','crossval','0.7255051421657592','0.04596424345908255','breast-cancer','stree_default','1','0','0.006016244888305664','0.0019705691370880303','{}','9.52','5.26','4.04');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:35:02','crossval','0.6029189726594865','0.023230292245275567','cardiotocography-10clases','stree_default','1','0','0.2563737440109253','0.030549892954076986','{}','30.08','15.54','12.12');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:35:04','crossval','0.8842913007456503','0.01580017229575517','cardiotocography-3clases','stree_default','1','0','0.03261420249938965','0.0030456065966004155','{}','9.52','5.26','5.12');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:35:05','crossval','0.771602787456446','0.0587094228135243','conn-bench-sonar-mines-rocks','stree_default','1','0','0.008208627700805665','0.0014543365229276186','{}','5.44','3.22','2.84');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:35:06','crossval','0.6843613173424711','0.0403270755872992','cylinder-bands','stree_default','1','0','0.019361248016357423','0.0036520455327941543','{}','3.52','2.26','2.24');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:35:07','crossval','0.9704627915586821','0.016521621206752043','dermatology','stree_default','1','0','0.01973677635192871','0.001430262224385611','{}','11.0','6.0','6.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:35:07','crossval','0.8490883190883189','0.0650355715837188','echocardiogram','stree_default','1','0','0.002098259925842285','0.0002801813495844156','{}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:35:07','crossval','0.88','0.0547722557505166','fertility','stree_default','1','0','0.0010959434509277344','0.00010282517673575151','{}','1.0','1.0','1.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:35:07','crossval','0.7336805922792174','0.049766281568050116','haberman-survival','stree_default','1','0','0.004379959106445313','0.0007666798954801904','{}','6.48','3.74','3.28');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:35:07','crossval','0.8282232612507304','0.04871940627905826','heart-hungarian','stree_default','1','0','0.004726200103759765','0.0005667095613275619','{}','4.36','2.68','2.66');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:35:08','crossval','0.7980645161290322','0.07378851575436417','hepatitis','stree_default','1','0','0.0035898303985595703','0.0008488643081805688','{}','4.64','2.82','2.64');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:35:08','crossval','0.7130518715001475','0.03723387507571413','ilpd-indian-liver','stree_default','1','0','0.0021433162689208983','0.0009167842284522654','{}','1.88','1.44','1.42');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:35:09','crossval','0.8846197183098593','0.03695714618235266','ionosphere','stree_default','1','0','0.024134411811828613','0.004866466374706728','{}','6.04','3.52','3.52');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:35:09','crossval','0.9520000000000001','0.029933259094191526','iris','stree_default','1','0','0.0033076858520507814','0.00029582688362781513','{}','5.0','3.0','3.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:35:16','crossval','0.6995999999999999','0.02761955828756136','led-display','stree_default','1','0','0.12607113838195802','0.005472823185785991','{}','34.76','17.88','13.3');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:37:56','crossval','0.6980555555555555','0.05851888478553918','libras','stree_default','1','0','3.2097356271743775','0.43909148547322907','{}','74.8','37.9','28.46');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:05','crossval','0.85102274731088','0.036115696547377084','low-res-spect','stree_default','1','0','0.1774393081665039','0.012323526018892136','{}','16.2','8.6','8.08');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:06','crossval','0.8472643678160919','0.060229288406275214','lymphography','stree_default','1','0','0.004910616874694824','0.0004481332429668406','{}','5.12','3.06','3.06');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:06','crossval','0.805308182210708','0.024897168922554405','mammographic','stree_default','1','0','0.0038238859176635744','0.0005213750640919884','{}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:06','crossval','0.7764502164502166','0.08351281060668775','molec-biol-promoter','stree_default','1','0','0.0025696802139282228','0.0001947309808086681','{}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:09','crossval','0.8176666666666668','0.03328145229619156','musk-1','stree_default','1','0','0.0647481346130371','0.009914213318021168','{}','3.68','2.34','2.34');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:12','crossval','0.7273825920612147','0.02528451350172609','oocytes_merluccius_nucleus_4d','stree_default','1','0','0.044887299537658694','0.012174511752956753','{}','5.52','3.26','2.92');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:14','crossval','0.9060664753706361','0.01947618443632987','oocytes_merluccius_states_2f','stree_default','1','0','0.03846145153045654','0.0021951081957765728','{}','6.04','3.52','3.52');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:14','crossval','0.7188584639404313','0.03644014184644201','oocytes_trisopterus_nucleus_2f','stree_default','1','0','0.017146553993225098','0.0032112272582977305','{}','3.6','2.3','2.24');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:17','crossval','0.8663327928901701','0.019963031818889542','oocytes_trisopterus_states_5b','stree_default','1','0','0.04839353084564209','0.004813875208843272','{}','3.08','2.04','2.04');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:17','crossval','0.8543589743589743','0.04957147066090082','parkinsons','stree_default','1','0','0.00293088436126709','0.00023454434469966087','{}','3.04','2.02','2.02');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:17','crossval','0.7688659706306764','0.030512242922947463','pima','stree_default','1','0','0.004739713668823242','0.00037445991977090354','{}','3.16','2.08','2.08');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:18','crossval','0.8628571428571427','0.07236919683323605','pittsburg-bridges-MATERIAL','stree_default','1','0','0.0037888479232788086','0.0005896370405092044','{}','4.48','2.74','2.74');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:18','crossval','0.6221904761904762','0.09159389814212923','pittsburg-bridges-REL-L','stree_default','1','0','0.0051055288314819335','0.0016710560826529023','{}','6.24','3.62','3.5');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:18','crossval','0.6707017543859649','0.08917867889965793','pittsburg-bridges-SPAN','stree_default','1','0','0.005823330879211426','0.0013286817122288473','{}','7.44','4.22','4.1');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:18','crossval','0.8638095238095238','0.07623659318588133','pittsburg-bridges-T-OR-D','stree_default','1','0','0.001626429557800293','0.0003492996023521205','{}','2.48','1.74','1.74');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:19','crossval','0.7138138138138139','0.07202043946309418','planning','stree_default','1','0','0.0016888856887817382','0.0004383059592913558','{}','1.16','1.08','1.08');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:19','crossval','0.691111111111111','0.07630348761506396','post-operative','stree_default','1','0','0.0023808193206787107','0.0011270186597491917','{}','2.92','1.96','1.96');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:19','crossval','0.928095238095238','0.041918287860346314','seeds','stree_default','1','0','0.004807629585266113','0.00026429551359908953','{}','5.0','3.0','3.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:19','crossval','0.6781159420289855','0.038917563767356014','statlog-australian-credit','stree_default','1','0','0.001938614845275879','0.0002762651374347175','{}','1.04','1.02','1.02');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:20','crossval','0.7695000000000001','0.027771388153997628','statlog-german-credit','stree_default','1','0','0.017238106727600098','0.0034538756976054017','{}','8.64','4.82','3.66');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:20','crossval','0.8425925925925926','0.039064857610609224','statlog-heart','stree_default','1','0','0.003095235824584961','0.00023447586536594583','{}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:25','crossval','0.86991341991342','0.016262600654772162','statlog-image','stree_default','1','0','0.09091208934783936','0.003672333020016789','{}','15.72','8.36','7.88');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:27','crossval','0.7022721893491126','0.033859340958035986','statlog-vehicle','stree_default','1','0','0.03200491905212402','0.0035948874525991064','{}','13.36','7.18','5.66');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:37','crossval','0.9553333333333335','0.018009256878986794','synthetic-control','stree_default','1','0','0.2033984375','0.013455634798339165','{}','11.8','6.4','6.36');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:37','crossval','0.983296247818499','0.008356787795559038','tic-tac-toe','stree_default','1','0','0.00591310977935791','0.0004437194561528769','{}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:37','crossval','0.8074193548387096','0.05185527767837077','vertebral-column-2clases','stree_default','1','0','0.0025585460662841796','8.885090446324012e-05','{}','3.0','2.0','2.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:38','crossval','0.9741746031746031','0.02684380414757996','wine','stree_default','1','0','0.0036084318161010744','0.00035897396435915694','{}','5.0','3.0','3.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:38:38','crossval','0.9574761904761904','0.046554797887534714','zoo','stree_default','1','0','0.006736030578613281','0.0004044812194375824','{}','13.88','7.44','7.44');

55
results/normalizados/stree_default.txt Normal file
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* Process all datasets set with stree_default: tanveer norm: True std: False store in: stree_default
5 Fold Cross Validation with 10 random seeds [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
Dataset Samp Var Cls Nodes Leaves Depth Accuracy Time Parameters
============================== ===== === === ======= ======= ======= =============== =============== ==========
balance-scale 625 4 3 16.32 8.66 6.68 0.907840±0.0270 0.014065±0.0034 {}
balloons 16 4 2 4.08 2.54 2.34 0.641667±0.2760 0.001445±0.0005 {}
breast-cancer-wisc-diag 569 30 2 3.00 2.00 2.00 0.966609±0.0166 0.004475±0.0005 {}
breast-cancer-wisc-prog 198 33 2 3.44 2.22 2.22 0.790846±0.0663 0.004706±0.0006 {}
breast-cancer-wisc 699 9 2 3.92 2.46 2.46 0.965663±0.0129 0.003395±0.0005 {}
breast-cancer 286 9 2 9.52 5.26 4.04 0.725505±0.0460 0.006016±0.0020 {}
cardiotocography-10clases 2126 21 10 30.08 15.54 12.12 0.602919±0.0232 0.256374±0.0305 {}
cardiotocography-3clases 2126 21 3 9.52 5.26 5.12 0.884291±0.0158 0.032614±0.0030 {}
conn-bench-sonar-mines-rocks 208 60 2 5.44 3.22 2.84 0.771603±0.0587 0.008209±0.0015 {}
cylinder-bands 512 35 2 3.52 2.26 2.24 0.684361±0.0403 0.019361±0.0037 {}
dermatology 366 34 6 11.00 6.00 6.00 0.970463±0.0165 0.019737±0.0014 {}
echocardiogram 131 10 2 3.00 2.00 2.00 0.849088±0.0650 0.002098±0.0003 {}
fertility 100 9 2 1.00 1.00 1.00 0.880000±0.0548 0.001096±0.0001 {}
haberman-survival 306 3 2 6.48 3.74 3.28 0.733681±0.0498 0.004380±0.0008 {}
heart-hungarian 294 12 2 4.36 2.68 2.66 0.828223±0.0487 0.004726±0.0006 {}
hepatitis 155 19 2 4.64 2.82 2.64 0.798065±0.0738 0.003590±0.0008 {}
ilpd-indian-liver 583 9 2 1.88 1.44 1.42 0.713052±0.0372 0.002143±0.0009 {}
ionosphere 351 33 2 6.04 3.52 3.52 0.884620±0.0370 0.024134±0.0049 {}
iris 150 4 3 5.00 3.00 3.00 0.952000±0.0299 0.003308±0.0003 {}
led-display 1000 7 10 34.76 17.88 13.30 0.699600±0.0276 0.126071±0.0055 {}
libras 360 90 15 74.80 37.90 28.46 0.698056±0.0585 3.209736±0.4391 {}
low-res-spect 531 100 9 16.20 8.60 8.08 0.851023±0.0361 0.177439±0.0123 {}
lymphography 148 18 4 5.12 3.06 3.06 0.847264±0.0602 0.004911±0.0004 {}
mammographic 961 5 2 3.00 2.00 2.00 0.805308±0.0249 0.003824±0.0005 {}
molec-biol-promoter 106 57 2 3.00 2.00 2.00 0.776450±0.0835 0.002570±0.0002 {}
musk-1 476 166 2 3.68 2.34 2.34 0.817667±0.0333 0.064748±0.0099 {}
oocytes_merluccius_nucleus_4d 1022 41 2 5.52 3.26 2.92 0.727383±0.0253 0.044887±0.0122 {}
oocytes_merluccius_states_2f 1022 25 3 6.04 3.52 3.52 0.906066±0.0195 0.038461±0.0022 {}
oocytes_trisopterus_nucleus_2f 912 25 2 3.60 2.30 2.24 0.718858±0.0364 0.017147±0.0032 {}
oocytes_trisopterus_states_5b 912 32 3 3.08 2.04 2.04 0.866333±0.0200 0.048394±0.0048 {}
parkinsons 195 22 2 3.04 2.02 2.02 0.854359±0.0496 0.002931±0.0002 {}
pima 768 8 2 3.16 2.08 2.08 0.768866±0.0305 0.004740±0.0004 {}
pittsburg-bridges-MATERIAL 106 7 3 4.48 2.74 2.74 0.862857±0.0724 0.003789±0.0006 {}
pittsburg-bridges-REL-L 103 7 3 6.24 3.62 3.50 0.622190±0.0916 0.005106±0.0017 {}
pittsburg-bridges-SPAN 92 7 3 7.44 4.22 4.10 0.670702±0.0892 0.005823±0.0013 {}
pittsburg-bridges-T-OR-D 102 7 2 2.48 1.74 1.74 0.863810±0.0762 0.001626±0.0003 {}
planning 182 12 2 1.16 1.08 1.08 0.713814±0.0720 0.001689±0.0004 {}
post-operative 90 8 3 2.92 1.96 1.96 0.691111±0.0763 0.002381±0.0011 {}
seeds 210 7 3 5.00 3.00 3.00 0.928095±0.0419 0.004808±0.0003 {}
statlog-australian-credit 690 14 2 1.04 1.02 1.02 0.678116±0.0389 0.001939±0.0003 {}
statlog-german-credit 1000 24 2 8.64 4.82 3.66 0.769500±0.0278 0.017238±0.0035 {}
statlog-heart 270 13 2 3.00 2.00 2.00 0.842593±0.0391 0.003095±0.0002 {}
statlog-image 2310 18 7 15.72 8.36 7.88 0.869913±0.0163 0.090912±0.0037 {}
statlog-vehicle 846 18 4 13.36 7.18 5.66 0.702272±0.0339 0.032005±0.0036 {}
synthetic-control 600 60 6 11.80 6.40 6.36 0.955333±0.0180 0.203398±0.0135 {}
tic-tac-toe 958 9 2 3.00 2.00 2.00 0.983296±0.0084 0.005913±0.0004 {}
vertebral-column-2clases 310 6 2 3.00 2.00 2.00 0.807419±0.0519 0.002559±0.0001 {}
wine 178 13 3 5.00 3.00 3.00 0.974175±0.0268 0.003608±0.0004 {}
zoo 101 16 7 13.88 7.44 7.44 0.957476±0.0466 0.006736±0.0004 {}
Time: 0h 3m 50s

49
results/normalizados/wodt.sql Normal file
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replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:13:53','crossval','0.9105599999999999','0.030929700936155203','balance-scale','wodt','1','0','0.21324075222015382','0.022901207386101848','{}','83.32','42.16','12.84');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:13:54','crossval','0.6266666666666667','0.23407026485414348','balloons','wodt','1','0','0.006847372055053711','0.002903877826101847','{}','6.28','3.64','3.62');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:14:00','crossval','0.9567706877814003','0.016914230172549206','breast-cancer-wisc-diag','wodt','1','0','0.12148640155792237','0.01934258481327128','{}','25.12','13.06','7.76');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:14:05','crossval','0.6870769230769231','0.06463654008582861','breast-cancer-wisc-prog','wodt','1','0','0.10070056438446046','0.009297048503327857','{}','41.16','21.08','9.26');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:14:12','crossval','0.9495077081192188','0.01703739203824271','breast-cancer-wisc','wodt','1','0','0.1322394847869873','0.017212358772069145','{}','36.8','18.9','9.74');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:14:27','crossval','0.6554325468844524','0.05487233490223692','breast-cancer','wodt','1','0','0.2974639749526978','0.02829323609286599','{}','421.76','211.38','50.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:16:26','crossval','0.7500512565589617','0.023847918777948455','cardiotocography-10clases','wodt','1','0','2.374505958557129','0.09044907986312886','{}','999.16','500.08','50.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:17:32','crossval','0.9022580502623583','0.01682397786753013','cardiotocography-3clases','wodt','1','0','1.2965420722961425','0.10332802112995174','{}','585.88','293.44','50.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:17:36','crossval','0.7971544715447153','0.05716696020561157','conn-bench-sonar-mines-rocks','wodt','1','0','0.08178516387939454','0.01793983528471189','{}','22.6','11.8','6.98');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:17:54','crossval','0.6945402627070247','0.03413508137216515','cylinder-bands','wodt','1','0','0.36812313079833986','0.023456447314929074','{}','101.16','51.08','13.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:17:59','crossval','0.95870788596816','0.02267092716846112','dermatology','wodt','1','0','0.08558360576629638','0.009163030020141403','{}','18.68','9.84','7.56');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:18:03','crossval','0.7328774928774928','0.0706950306289071','echocardiogram','wodt','1','0','0.07621054649353028','0.010653465843640943','{}','35.08','18.04','10.82');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:18:05','crossval','0.8019999999999999','0.0720832851637604','fertility','wodt','1','0','0.052550797462463376','0.009865634043983385','{}','79.16','40.08','35.56');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:18:26','crossval','0.6616869381279745','0.06504078462800772','haberman-survival','wodt','1','0','0.4164604330062866','0.03465050330440698','{}','476.76','238.88','50.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:18:36','crossval','0.7482758620689656','0.04934172609981429','heart-hungarian','wodt','1','0','0.20049129486083983','0.023078445752833795','{}','73.4','37.2','12.3');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:18:39','crossval','0.7877419354838708','0.08657910676004532','hepatitis','wodt','1','0','0.051876163482666014','0.006903293617037604','{}','22.68','11.84','6.88');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:19:16','crossval','0.6715517241379311','0.04266963563291409','ilpd-indian-liver','wodt','1','0','0.7394672966003418','0.04079052300663627','{}','647.04','324.02','50.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:19:23','crossval','0.8881086519114689','0.03764527302298102','ionosphere','wodt','1','0','0.1414583683013916','0.017158020866605343','{}','32.56','16.78','10.42');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:19:26','crossval','0.944','0.03555277766926235','iris','wodt','1','0','0.04254323959350586','0.0085627389063986','{}','16.6','8.8','7.38');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:20:57','crossval','0.7014999999999999','0.029584624384974015','led-display','wodt','1','0','1.7910533094406127','0.04404600358780579','{}','4097.88','2049.44','50.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:21:15','crossval','0.7761111111111112','0.05201495511442412','libras','wodt','1','0','0.3663883113861084','0.021033614938297272','{}','131.72','66.36','12.58');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:21:32','crossval','0.8570410862281785','0.03395969665674335','low-res-spect','wodt','1','0','0.3388829708099365','0.025404722162263967','{}','93.12','47.06','10.98');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:21:35','crossval','0.809448275862069','0.08117100751718888','lymphography','wodt','1','0','0.05676698684692383','0.007699755790549307','{}','22.24','11.62','7.34');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:22:33','crossval','0.7806417314335059','0.023703496492134483','mammographic','wodt','1','0','1.132075505256653','0.07506506634511496','{}','2107.8','1054.4','50.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:22:33','crossval','0.8019913419913419','0.08390146472090296','molec-biol-promoter','wodt','1','0','0.014979376792907714','0.0037749224410426807','{}','6.44','3.72','3.3');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:22:45','crossval','0.8418048245614035','0.03894767594364527','musk-1','wodt','1','0','0.21523403644561767','0.014357181162113004','{}','55.04','28.02','9.7');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:23:39','crossval','0.7143758967001433','0.031167138044523302','oocytes_merluccius_nucleus_4d','wodt','1','0','1.077880754470825','0.048071859340309914','{}','370.0','185.5','19.66');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:24:02','crossval','0.8991190817790531','0.018392253684405998','oocytes_merluccius_states_2f','wodt','1','0','0.45797646045684814','0.031076144718348856','{}','122.72','61.86','14.18');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:24:49','crossval','0.7302618146880442','0.029528774344107375','oocytes_trisopterus_nucleus_2f','wodt','1','0','0.9343574523925782','0.04572818300741046','{}','296.64','148.82','20.32');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:25:21','crossval','0.8775193658800216','0.024559755091526626','oocytes_trisopterus_states_5b','wodt','1','0','0.6305027294158936','0.042468646638130644','{}','154.44','77.72','16.4');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:25:24','crossval','0.901025641025641','0.04810956157075134','parkinsons','wodt','1','0','0.059291563034057616','0.01055478740684769','{}','22.16','11.58','8.5');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:25:59','crossval','0.698935574229692','0.03230807837032946','pima','wodt','1','0','0.6876588249206543','0.03466577583613701','{}','253.52','127.26','18.34');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:26:02','crossval','0.8175757575757575','0.07645815826535769','pittsburg-bridges-MATERIAL','wodt','1','0','0.06273589611053466','0.012351463710196854','{}','77.2','39.1','32.76');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:26:08','crossval','0.6077142857142858','0.10174727483578092','pittsburg-bridges-REL-L','wodt','1','0','0.1229737377166748','0.013397986454497272','{}','118.08','59.54','36.44');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:26:14','crossval','0.5921637426900584','0.09595142290915461','pittsburg-bridges-SPAN','wodt','1','0','0.11180374145507813','0.012817073494334351','{}','157.24','79.12','47.66');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:26:17','crossval','0.8324761904761904','0.061685987045133894','pittsburg-bridges-T-OR-D','wodt','1','0','0.05939210414886475','0.010921283462823625','{}','79.88','40.44','34.28');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:26:25','crossval','0.5642642642642642','0.0841539017305423','planning','wodt','1','0','0.172938551902771','0.022598157656707425','{}','77.76','39.38','14.24');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:26:33','crossval','0.5544444444444445','0.09328702554912162','post-operative','wodt','1','0','0.14343581199645997','0.021526588871244652','{}','362.16','181.58','50.0');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:26:36','crossval','0.9223809523809523','0.04359419129585074','seeds','wodt','1','0','0.06962965965270997','0.008967647298677685','{}','25.12','13.06','6.88');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:27:17','crossval','0.5624637681159421','0.041775597998254206','statlog-australian-credit','wodt','1','0','0.808171067237854','0.05310326177690986','{}','441.88','221.44','48.14');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:27:48','crossval','0.6877000000000001','0.03127634889177442','statlog-german-credit','wodt','1','0','0.611791033744812','0.03868067279916215','{}','198.88','99.94','16.42');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:27:54','crossval','0.7674074074074076','0.048454863530151965','statlog-heart','wodt','1','0','0.1247534704208374','0.010010913983272234','{}','48.56','24.78','9.7');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:28:54','crossval','0.9462770562770564','0.010028818503552679','statlog-image','wodt','1','0','1.1948295736312866','0.07319053510178329','{}','320.24','160.62','43.02');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:29:31','crossval','0.7055718760877131','0.035849202498875715','statlog-vehicle','wodt','1','0','0.7270735836029053','0.03821371885242998','{}','258.44','129.72','16.06');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:29:40','crossval','0.9500000000000002','0.019860625479688296','synthetic-control','wodt','1','0','0.16990867137908935','0.017103112034262763','{}','26.92','13.96','6.72');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:29:58','crossval','0.9180404668411868','0.026601040576775932','tic-tac-toe','wodt','1','0','0.3678532075881958','0.037207143935023695','{}','87.32','44.16','13.62');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:30:09','crossval','0.8019354838709677','0.05008213856793639','vertebral-column-2clases','wodt','1','0','0.21485376358032227','0.019045788752517546','{}','104.68','52.84','22.92');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:30:11','crossval','0.9719206349206349','0.029239495511339145','wine','wodt','1','0','0.02917142391204834','0.005861948918698455','{}','8.08','4.54','3.88');
replace into results (date,time,type,accuracy,accuracy_std,dataset,classifier,norm,stand,time_spent,time_spent_std,parameters,nodes,leaves,depth) values('2021-04-11','00:30:12','crossval','0.9393809523809523','0.051272271307097966','zoo','wodt','1','0','0.03375922203063965','0.00414028943244922','{}','16.52','8.76','6.0');

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* Process all datasets set with wodt: tanveer norm: True std: False store in: wodt
5 Fold Cross Validation with 10 random seeds [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
Dataset Samp Var Cls Nodes Leaves Depth Accuracy Time Parameters
============================== ===== === === ======= ======= ======= =============== =============== ==========
balance-scale 625 4 3 83.32 42.16 12.84 0.910560±0.0309 0.213241±0.0229 {}
balloons 16 4 2 6.28 3.64 3.62 0.626667±0.2341 0.006847±0.0029 {}
breast-cancer-wisc-diag 569 30 2 25.12 13.06 7.76 0.956771±0.0169 0.121486±0.0193 {}
breast-cancer-wisc-prog 198 33 2 41.16 21.08 9.26 0.687077±0.0646 0.100701±0.0093 {}
breast-cancer-wisc 699 9 2 36.80 18.90 9.74 0.949508±0.0170 0.132239±0.0172 {}
breast-cancer 286 9 2 421.76 211.38 50.00 0.655433±0.0549 0.297464±0.0283 {}
cardiotocography-10clases 2126 21 10 999.16 500.08 50.00 0.750051±0.0238 2.374506±0.0904 {}
cardiotocography-3clases 2126 21 3 585.88 293.44 50.00 0.902258±0.0168 1.296542±0.1033 {}
conn-bench-sonar-mines-rocks 208 60 2 22.60 11.80 6.98 0.797154±0.0572 0.081785±0.0179 {}
cylinder-bands 512 35 2 101.16 51.08 13.00 0.694540±0.0341 0.368123±0.0235 {}
dermatology 366 34 6 18.68 9.84 7.56 0.958708±0.0227 0.085584±0.0092 {}
echocardiogram 131 10 2 35.08 18.04 10.82 0.732877±0.0707 0.076211±0.0107 {}
fertility 100 9 2 79.16 40.08 35.56 0.802000±0.0721 0.052551±0.0099 {}
haberman-survival 306 3 2 476.76 238.88 50.00 0.661687±0.0650 0.416460±0.0347 {}
heart-hungarian 294 12 2 73.40 37.20 12.30 0.748276±0.0493 0.200491±0.0231 {}
hepatitis 155 19 2 22.68 11.84 6.88 0.787742±0.0866 0.051876±0.0069 {}
ilpd-indian-liver 583 9 2 647.04 324.02 50.00 0.671552±0.0427 0.739467±0.0408 {}
ionosphere 351 33 2 32.56 16.78 10.42 0.888109±0.0376 0.141458±0.0172 {}
iris 150 4 3 16.60 8.80 7.38 0.944000±0.0356 0.042543±0.0086 {}
led-display 1000 7 10 4097.88 2049.44 50.00 0.701500±0.0296 1.791053±0.0440 {}
libras 360 90 15 131.72 66.36 12.58 0.776111±0.0520 0.366388±0.0210 {}
low-res-spect 531 100 9 93.12 47.06 10.98 0.857041±0.0340 0.338883±0.0254 {}
lymphography 148 18 4 22.24 11.62 7.34 0.809448±0.0812 0.056767±0.0077 {}
mammographic 961 5 2 2107.80 1054.40 50.00 0.780642±0.0237 1.132076±0.0751 {}
molec-biol-promoter 106 57 2 6.44 3.72 3.30 0.801991±0.0839 0.014979±0.0038 {}
musk-1 476 166 2 55.04 28.02 9.70 0.841805±0.0389 0.215234±0.0144 {}
oocytes_merluccius_nucleus_4d 1022 41 2 370.00 185.50 19.66 0.714376±0.0312 1.077881±0.0481 {}
oocytes_merluccius_states_2f 1022 25 3 122.72 61.86 14.18 0.899119±0.0184 0.457976±0.0311 {}
oocytes_trisopterus_nucleus_2f 912 25 2 296.64 148.82 20.32 0.730262±0.0295 0.934357±0.0457 {}
oocytes_trisopterus_states_5b 912 32 3 154.44 77.72 16.40 0.877519±0.0246 0.630503±0.0425 {}
parkinsons 195 22 2 22.16 11.58 8.50 0.901026±0.0481 0.059292±0.0106 {}
pima 768 8 2 253.52 127.26 18.34 0.698936±0.0323 0.687659±0.0347 {}
pittsburg-bridges-MATERIAL 106 7 3 77.20 39.10 32.76 0.817576±0.0765 0.062736±0.0124 {}
pittsburg-bridges-REL-L 103 7 3 118.08 59.54 36.44 0.607714±0.1017 0.122974±0.0134 {}
pittsburg-bridges-SPAN 92 7 3 157.24 79.12 47.66 0.592164±0.0960 0.111804±0.0128 {}
pittsburg-bridges-T-OR-D 102 7 2 79.88 40.44 34.28 0.832476±0.0617 0.059392±0.0109 {}
planning 182 12 2 77.76 39.38 14.24 0.564264±0.0842 0.172939±0.0226 {}
post-operative 90 8 3 362.16 181.58 50.00 0.554444±0.0933 0.143436±0.0215 {}
seeds 210 7 3 25.12 13.06 6.88 0.922381±0.0436 0.069630±0.0090 {}
statlog-australian-credit 690 14 2 441.88 221.44 48.14 0.562464±0.0418 0.808171±0.0531 {}
statlog-german-credit 1000 24 2 198.88 99.94 16.42 0.687700±0.0313 0.611791±0.0387 {}
statlog-heart 270 13 2 48.56 24.78 9.70 0.767407±0.0485 0.124753±0.0100 {}
statlog-image 2310 18 7 320.24 160.62 43.02 0.946277±0.0100 1.194830±0.0732 {}
statlog-vehicle 846 18 4 258.44 129.72 16.06 0.705572±0.0358 0.727074±0.0382 {}
synthetic-control 600 60 6 26.92 13.96 6.72 0.950000±0.0199 0.169909±0.0171 {}
tic-tac-toe 958 9 2 87.32 44.16 13.62 0.918040±0.0266 0.367853±0.0372 {}
vertebral-column-2clases 310 6 2 104.68 52.84 22.92 0.801935±0.0501 0.214854±0.0190 {}
wine 178 13 3 8.08 4.54 3.88 0.971921±0.0292 0.029171±0.0059 {}
zoo 101 16 7 16.52 8.76 6.00 0.939381±0.0513 0.033759±0.0041 {}
Time: 0h 16m 29s