Doctorado-ML/stree_datasets
1
0
Fork 0
mirror of https://github.com/Doctorado-ML/Stree_datasets.git synced 2025-08-17 00:16:09 +00:00
Code Issues Packages Projects Releases Wiki Activity

add results folder

Browse Source
This commit is contained in:
Ricardo Montañana Gómez
2021-04-11 02:24:03 +02:00
parent dd83175508
commit 603c73a476
23 changed files with 1202 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

49
results/no_normalizados/base_raf.sql Normal file
View File

@@ -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);

57
results/no_normalizados/stree.txt Normal file
View File

@@ -0,0 +1,57 @@
* 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
View File

@@ -0,0 +1,57 @@
* 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
View File

@@ -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('2021-04-10','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
View File

@@ -0,0 +1,56 @@
* 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