Doctorado-ML/stree_datasets
1
0
Fork 0
mirror of https://github.com/Doctorado-ML/Stree_datasets.git synced 2025-08-15 15:36:01 +00:00
Code Issues Packages Projects Releases Wiki Activity

Add oc1 norm/no_norm results

Browse Source
This commit is contained in:
Ricardo Montañana Gómez
2021-04-11 15:03:46 +02:00
parent 08e9e45048
commit 6fedf7a604
4 changed files with 314 additions and 98 deletions
Download Patch File Download Diff File Expand all files Collapse all files

49
results/no_normalizados/oc1.sql Normal file
View File

@@ -0,0 +1,49 @@
insert 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","13:43:28","crossval","0.919199988","0.022928468998468485","balance-scale","oc1","1","0","0.15758534","0.022345369377952624","{}","19.4","6.2","7.2");
insert 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","13:43:28","crossval","0.619999972","0.2609071998773636","balloons","oc1","1","0","0.00030844","0.0002348338674597905","{}","2.2","1.0","2.2");
insert 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","13:43:28","crossval","0.933477046","0.026311682939621855","breast-cancer-wisc-diag","oc1","1","0","0.4406648","0.06350629207104005","{}","8.0","3.0","4.4");
insert 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","13:43:28","crossval","0.709999984","0.07955812149382789","breast-cancer-wisc-prog","oc1","1","0","0.16363574","0.036606637163787994","{}","11.4","4.2","5.8");
insert 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","13:43:28","crossval","0.94019421","0.02100409938015746","breast-cancer-wisc","oc1","1","0","0.19944314","0.029906694694523308","{}","11.2","4.0","6.6");
insert 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","13:43:28","crossval","0.64972776","0.06789902609753301","breast-cancer","oc1","1","0","0.09331238","0.01389738530376216","{}","23.0","6.4","8.4");
insert 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","13:43:28","crossval","0.795527874","0.019194464349696225","cardiotocography-10clases","oc1","1","0","4.3503065","0.27225653156770274","{}","122.4","41.4","13.4");
insert 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","13:43:28","crossval","0.899811318","0.016266190571653297","cardiotocography-3clases","oc1","1","0","2.42902948","0.40715254316028454","{}","52.4","18.0","10.2");
insert 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","13:43:28","crossval","0.710798238","0.07164378928650933","conn-bench-sonar-mines-rocks","oc1","1","0","0.10360458","0.005299372073276266","{}","10.8","4.0","5.0");
insert 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","13:43:28","crossval","0.671059568","0.04175041919191456","cylinder-bands","oc1","1","0","0.47492386000000003","0.08216321474793141","{}","36.8","12.6","9.8");
insert 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","13:43:28","crossval","0.916086654","0.04339652757793572","dermatology","oc1","1","0","0.11251114","0.0159243193863592","{}","5.4","2.8","3.6");
insert 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","13:43:28","crossval","0.74829059","0.086689318067258","echocardiogram","oc1","1","0","0.02473912","0.005133546359993653","{}","7.6","2.4","5.2");
insert 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","13:43:28","crossval","0.793","0.08018473568413563","fertility","oc1","1","0","0.01644868","0.004574846246730538","{}","5.8","2.0","4.4");
insert 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","13:43:28","crossval","0.651634068","0.05782825764577645","haberman-survival","oc1","1","0","0.0631877","0.0072805839422958605","{}","25.4","6.6","8.2");
insert 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","13:43:28","crossval","0.758298106","0.04751287557647803","heart-hungarian","oc1","1","0","0.08981114","0.01770158774385703","{}","16.0","4.8","6.0");
insert 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","13:43:28","crossval","0.756774218","0.07633927816039446","hepatitis","oc1","1","0","0.03985772","0.011435038445345543","{}","8.0","2.4","4.8");
insert 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","13:43:28","crossval","0.66013908","0.050216248258669494","ilpd-indian-liver","oc1","1","0","0.30552824","0.029610812070800168","{}","38.6","11.4","10.0");
insert 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","13:43:28","crossval","0.879742456","0.04078499717935586","ionosphere","oc1","1","0","0.2697913","0.03446318475946914","{}","8.6","2.4","6.0");
insert 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","13:43:28","crossval","0.947999964","0.047217354202340585","iris","oc1","1","0","0.01697568","0.003151379752503623","{}","3.6","1.0","3.6");
insert 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","13:43:28","crossval","0.6993","0.030572930537203204","led-display","oc1","1","0","0.29797592","0.012956706889562675","{}","62.6","23.4","8.2");
insert 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","13:43:28","crossval","0.644999996","0.06212313913693778","libras","oc1","1","0","0.5830844","0.10705255659187753","{}","50.2","18.0","8.8");
insert 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","13:43:28","crossval","0.824671134","0.034387913920443644","low-res-spect","oc1","1","0","1.21806182","0.1894391624213043","{}","32.2","11.4","7.4");
insert 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","13:43:28","crossval","0.734633624","0.07505967701227854","lymphography","oc1","1","0","0.03662084","0.006085540069136117","{}","7.2","2.6","4.4");
insert 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","13:43:28","crossval","0.768805056","0.042451008108027544","mammographic","oc1","1","0","0.40330064","0.0304541497247845","{}","84.0","21.2","12.6");
insert 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","13:43:28","crossval","0.73480519","0.07929681982000171","molec-biol-promoter","oc1","1","0","0.00308648","0.00024732926413580575","{}","9.8","3.4","4.8");
insert 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","13:43:28","crossval","0.77640132","0.04136666906353273","musk-1","oc1","1","0","0.75021326","0.05405970914733343","{}","28.8","9.4","11.6");
insert 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","13:43:28","crossval","0.74319913","0.038637374982475575","oocytes_merluccius_nucleus_4d","oc1","1","0","1.4739426","0.20908235653650248","{}","70.2","21.4","11.8");
insert 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","13:43:28","crossval","0.889223286","0.021904213371842132","oocytes_merluccius_states_2f","oc1","1","0","1.0546264","0.18662517073458842","{}","32.6","9.6","9.2");
insert 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","13:43:28","crossval","0.747697088","0.03432152551574619","oocytes_trisopterus_nucleus_2f","oc1","1","0","1.0034749","0.10601749965660753","{}","52.8","15.6","11.8");
insert 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","13:43:28","crossval","0.86392976","0.02062502994634808","oocytes_trisopterus_states_5b","oc1","1","0","1.3526446","0.20447791710418342","{}","38.4","13.0","12.0");
insert 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","13:43:28","crossval","0.86564104","0.0549597058415768","parkinsons","oc1","1","0","0.0899414","0.01691889924773778","{}","6.2","2.4","4.0");
insert 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","13:43:28","crossval","0.693026632","0.03464965512793265","pima","oc1","1","0","0.3920624","0.03647972198414917","{}","50.6","15.4","9.8");
insert 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","13:43:28","crossval","0.8102597420000001","0.08791120411801688","pittsburg-bridges-MATERIAL","oc1","1","0","0.01630412","0.003393762619866039","{}","6.2","2.4","3.8");
insert 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","13:43:28","crossval","0.604956518","0.12099535114803797","pittsburg-bridges-REL-L","oc1","1","0","0.0246469","0.0034962164987486755","{}","11.8","3.6","7.0");
insert 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","13:43:28","crossval","0.579333326","0.096773599093883","pittsburg-bridges-SPAN","oc1","1","0","0.01922252","0.0027835986855954974","{}","11.8","4.0","5.6");
insert 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","13:43:28","crossval","0.831545454","0.08260500811202823","pittsburg-bridges-T-OR-D","oc1","1","0","0.01537292","0.004610019216569803","{}","9.2","2.8","5.6");
insert 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","13:43:28","crossval","0.5669882980000001","0.09819126087918144","planning","oc1","1","0","0.07031054","0.012096526938247818","{}","16.0","5.2","7.8");
insert 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","13:43:28","crossval","0.542222202","0.1277555874609372","post-operative","oc1","1","0","0.01706538","0.004090332304698133","{}","13.8","4.0","6.0");
insert 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","13:43:28","crossval","0.932380962","0.03674729708467471","seeds","oc1","1","0","0.03468252","0.003911751939257616","{}","4.8","2.2","3.6");
insert 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","13:43:28","crossval","0.573913046","0.058026443678065934","statlog-australian-credit","oc1","1","0","0.41469248000000003","0.04788899725544737","{}","50.4","15.0","8.8");
insert 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","13:43:28","crossval","0.6874","0.038028453364633716","statlog-german-credit","oc1","1","0","0.939049","0.1432174301094069","{}","62.4","18.0","13.4");
insert 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","13:43:28","crossval","0.749259256","0.05822632907936435","statlog-heart","oc1","1","0","0.08672722","0.010369955067090184","{}","12.0","4.4","5.6");
insert 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","13:43:28","crossval","0.950129852","0.009469844490506888","statlog-image","oc1","1","0","2.34150554","0.1429278477253342","{}","43.8","16.2","11.6");
insert 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","13:43:28","crossval","0.70849635","0.03707889637712277","statlog-vehicle","oc1","1","0","0.85610944","0.06409603741808388","{}","62.0","18.6","11.8");
insert 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","13:43:28","crossval","0.863166664","0.037233171992205105","synthetic-control","oc1","1","0","1.40889312","0.08426795206246342","{}","18.8","6.4","6.8");
insert 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","13:43:28","crossval","0.918490308","0.03719306955411836","tic-tac-toe","oc1","1","0","0.3742426","0.06761291997684997","{}","20.4","7.2","8.2");
insert 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","13:43:28","crossval","0.815161292","0.047132057674445754","vertebral-column-2clases","oc1","1","0","0.07522561999999999","0.00795117969399022","{}","14.0","4.0","6.0");
insert 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","13:43:28","crossval","0.916165406","0.04659011245811016","wine","oc1","1","0","0.0363267","0.005601708344563382","{}","4.4","1.6","3.4");
insert 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","13:43:28","crossval","0.890952386","0.07664919395809533","zoo","oc1","1","0","0.01431152","0.0020010060453355427","{}","6.4","2.8","3.8");

108
results/no_normalizados/oc1.txt Normal file
View File

@@ -0,0 +1,108 @@
Generating csv datasets
========================
Generating: balance-scale, balloons, breast-cancer-wisc-diag, breast-cancer-wisc-prog, breast-cancer-wisc, breast-cancer, cardiotocography-10clases, cardiotocography-3clases, conn-bench-sonar-mines-rocks, cylinder-bands, dermatology, echocardiogram, fertility, haberman-survival, heart-hungarian, hepatitis, ilpd-indian-liver, ionosphere, iris, led-display, libras, low-res-spect, lymphography, mammographic, molec-biol-promoter, musk-1, oocytes_merluccius_nucleus_4d, oocytes_merluccius_states_2f, oocytes_trisopterus_nucleus_2f, oocytes_trisopterus_states_5b, parkinsons, pima, pittsburg-bridges-MATERIAL, pittsburg-bridges-REL-L, pittsburg-bridges-SPAN, pittsburg-bridges-T-OR-D, planning, post-operative, seeds, statlog-australian-credit, statlog-german-credit,
statlog-heart, statlog-image, statlog-vehicle, synthetic-control, tic-tac-toe, vertebral-column-2clases, wine, zoo, end
Processing datasets
===================
*balance-scale : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*balloons : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*breast-cancer-wisc-diag : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*breast-cancer-wisc-prog : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*breast-cancer-wisc : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*breast-cancer : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*cardiotocography-10clases : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*cardiotocography-3clases : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*conn-bench-sonar-mines-rocks : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*cylinder-bands : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*dermatology : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*echocardiogram : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*fertility : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*haberman-survival : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*heart-hungarian : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*hepatitis : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*ilpd-indian-liver : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*ionosphere : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*iris : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*led-display : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*libras : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*low-res-spect : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*lymphography : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*mammographic : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*molec-biol-promoter : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*musk-1 : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*oocytes_merluccius_nucleus_4d : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*oocytes_merluccius_states_2f : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*oocytes_trisopterus_nucleus_2f: 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*oocytes_trisopterus_states_5b : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*parkinsons : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*pima : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*pittsburg-bridges-MATERIAL : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*pittsburg-bridges-REL-L : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*pittsburg-bridges-SPAN : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*pittsburg-bridges-T-OR-D : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*planning : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*post-operative : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*seeds : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*statlog-australian-credit : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*statlog-german-credit : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*statlog-heart : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*statlog-image : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*statlog-vehicle : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*synthetic-control : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*tic-tac-toe : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*vertebral-column-2clases : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*wine : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*zoo : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Generating SQL
==============
Processing balance-scale nodes= 19.40 leaves= 6.20 depth= 7.20 accuracy=[0.919±0.023] time=[0.158±0.022] elements=[50 50]
Processing balloons nodes= 2.20 leaves= 1.00 depth= 2.20 accuracy=[0.620±0.261] time=[0.000±0.000] elements=[50 50]
Processing breast-cancer-wisc-diag nodes= 8.00 leaves= 3.00 depth= 4.40 accuracy=[0.933±0.026] time=[0.441±0.064] elements=[50 50]
Processing breast-cancer-wisc-prog nodes= 11.40 leaves= 4.20 depth= 5.80 accuracy=[0.710±0.080] time=[0.164±0.037] elements=[50 50]
Processing breast-cancer-wisc nodes= 11.20 leaves= 4.00 depth= 6.60 accuracy=[0.940±0.021] time=[0.199±0.030] elements=[50 50]
Processing breast-cancer nodes= 23.00 leaves= 6.40 depth= 8.40 accuracy=[0.650±0.068] time=[0.093±0.014] elements=[50 50]
Processing cardiotocography-10clases nodes=122.40 leaves= 41.40 depth=13.40 accuracy=[0.796±0.019] time=[4.350±0.272] elements=[50 50]
Processing cardiotocography-3clases nodes= 52.40 leaves= 18.00 depth=10.20 accuracy=[0.900±0.016] time=[2.429±0.407] elements=[50 50]
Processing conn-bench-sonar-mines-rocks nodes= 10.80 leaves= 4.00 depth= 5.00 accuracy=[0.711±0.072] time=[0.104±0.005] elements=[50 50]
Processing cylinder-bands nodes= 36.80 leaves= 12.60 depth= 9.80 accuracy=[0.671±0.042] time=[0.475±0.082] elements=[50 50]
Processing dermatology nodes= 5.40 leaves= 2.80 depth= 3.60 accuracy=[0.916±0.043] time=[0.113±0.016] elements=[50 50]
Processing echocardiogram nodes= 7.60 leaves= 2.40 depth= 5.20 accuracy=[0.748±0.087] time=[0.025±0.005] elements=[50 50]
Processing fertility nodes= 5.80 leaves= 2.00 depth= 4.40 accuracy=[0.793±0.080] time=[0.016±0.005] elements=[50 50]
Processing haberman-survival nodes= 25.40 leaves= 6.60 depth= 8.20 accuracy=[0.652±0.058] time=[0.063±0.007] elements=[50 50]
Processing heart-hungarian nodes= 16.00 leaves= 4.80 depth= 6.00 accuracy=[0.758±0.048] time=[0.090±0.018] elements=[50 50]
Processing hepatitis nodes= 8.00 leaves= 2.40 depth= 4.80 accuracy=[0.757±0.076] time=[0.040±0.011] elements=[50 50]
Processing ilpd-indian-liver nodes= 38.60 leaves= 11.40 depth=10.00 accuracy=[0.660±0.050] time=[0.306±0.030] elements=[50 50]
Processing ionosphere nodes= 8.60 leaves= 2.40 depth= 6.00 accuracy=[0.880±0.041] time=[0.270±0.034] elements=[50 50]
Processing iris nodes= 3.60 leaves= 1.00 depth= 3.60 accuracy=[0.948±0.047] time=[0.017±0.003] elements=[50 50]
Processing led-display nodes= 62.60 leaves= 23.40 depth= 8.20 accuracy=[0.699±0.031] time=[0.298±0.013] elements=[50 50]
Processing libras nodes= 50.20 leaves= 18.00 depth= 8.80 accuracy=[0.645±0.062] time=[0.583±0.107] elements=[50 50]
Processing low-res-spect nodes= 32.20 leaves= 11.40 depth= 7.40 accuracy=[0.825±0.034] time=[1.218±0.189] elements=[50 50]
Processing lymphography nodes= 7.20 leaves= 2.60 depth= 4.40 accuracy=[0.735±0.075] time=[0.037±0.006] elements=[50 50]
Processing mammographic nodes= 84.00 leaves= 21.20 depth=12.60 accuracy=[0.769±0.042] time=[0.403±0.030] elements=[50 50]
Processing molec-biol-promoter nodes= 9.80 leaves= 3.40 depth= 4.80 accuracy=[0.735±0.079] time=[0.003±0.000] elements=[50 50]
Processing musk-1 nodes= 28.80 leaves= 9.40 depth=11.60 accuracy=[0.776±0.041] time=[0.750±0.054] elements=[50 50]
Processing oocytes_merluccius_nucleus_4d nodes= 70.20 leaves= 21.40 depth=11.80 accuracy=[0.743±0.039] time=[1.474±0.209] elements=[50 50]
Processing oocytes_merluccius_states_2f nodes= 32.60 leaves= 9.60 depth= 9.20 accuracy=[0.889±0.022] time=[1.055±0.187] elements=[50 50]
Processing oocytes_trisopterus_nucleus_2f nodes= 52.80 leaves= 15.60 depth=11.80 accuracy=[0.748±0.034] time=[1.003±0.106] elements=[50 50]
Processing oocytes_trisopterus_states_5b nodes= 38.40 leaves= 13.00 depth=12.00 accuracy=[0.864±0.021] time=[1.353±0.204] elements=[50 50]
Processing parkinsons nodes= 6.20 leaves= 2.40 depth= 4.00 accuracy=[0.866±0.055] time=[0.090±0.017] elements=[50 50]
Processing pima nodes= 50.60 leaves= 15.40 depth= 9.80 accuracy=[0.693±0.035] time=[0.392±0.036] elements=[50 50]
Processing pittsburg-bridges-MATERIAL nodes= 6.20 leaves= 2.40 depth= 3.80 accuracy=[0.810±0.088] time=[0.016±0.003] elements=[50 50]
Processing pittsburg-bridges-REL-L nodes= 11.80 leaves= 3.60 depth= 7.00 accuracy=[0.605±0.121] time=[0.025±0.003] elements=[50 50]
Processing pittsburg-bridges-SPAN nodes= 11.80 leaves= 4.00 depth= 5.60 accuracy=[0.579±0.097] time=[0.019±0.003] elements=[50 50]
Processing pittsburg-bridges-T-OR-D nodes= 9.20 leaves= 2.80 depth= 5.60 accuracy=[0.832±0.083] time=[0.015±0.005] elements=[50 50]
Processing planning nodes= 16.00 leaves= 5.20 depth= 7.80 accuracy=[0.567±0.098] time=[0.070±0.012] elements=[50 50]
Processing post-operative nodes= 13.80 leaves= 4.00 depth= 6.00 accuracy=[0.542±0.128] time=[0.017±0.004] elements=[50 50]
Processing seeds nodes= 4.80 leaves= 2.20 depth= 3.60 accuracy=[0.932±0.037] time=[0.035±0.004] elements=[50 50]
Processing statlog-australian-credit nodes= 50.40 leaves= 15.00 depth= 8.80 accuracy=[0.574±0.058] time=[0.415±0.048] elements=[50 50]
Processing statlog-german-credit nodes= 62.40 leaves= 18.00 depth=13.40 accuracy=[0.687±0.038] time=[0.939±0.143] elements=[50 50]
Processing statlog-heart nodes= 12.00 leaves= 4.40 depth= 5.60 accuracy=[0.749±0.058] time=[0.087±0.010] elements=[50 50]
Processing statlog-image nodes= 43.80 leaves= 16.20 depth=11.60 accuracy=[0.950±0.009] time=[2.342±0.143] elements=[50 50]
Processing statlog-vehicle nodes= 62.00 leaves= 18.60 depth=11.80 accuracy=[0.708±0.037] time=[0.856±0.064] elements=[50 50]
Processing synthetic-control nodes= 18.80 leaves= 6.40 depth= 6.80 accuracy=[0.863±0.037] time=[1.409±0.084] elements=[50 50]
Processing tic-tac-toe nodes= 20.40 leaves= 7.20 depth= 8.20 accuracy=[0.918±0.037] time=[0.374±0.068] elements=[50 50]
Processing vertebral-column-2clases nodes= 14.00 leaves= 4.00 depth= 6.00 accuracy=[0.815±0.047] time=[0.075±0.008] elements=[50 50]
Processing wine nodes= 4.40 leaves= 1.60 depth= 3.40 accuracy=[0.916±0.047] time=[0.036±0.006] elements=[50 50]
Processing zoo nodes= 6.40 leaves= 2.80 depth= 3.80 accuracy=[0.891±0.077] time=[0.014±0.002] elements=[50 50]

98
results/normalizados/oc1.sql
View File

@@ -1,49 +1,49 @@
insert 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","11:15:07","crossval","0.914399992","0.029593164241038455","balance-scale","oc1","1","0","0.17182074","0.023016700220784676","{}","40.2","20.6","7.4"); insert 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","14:47:28","crossval","0.914399992","0.029593164241038455","balance-scale","oc1","1","0","0.17813794","0.023289144146532288","{}","19.6","6.6","7.4");
insert 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","11:15:07","crossval","0.589999968","0.26073331346959727","balloons","oc1","1","0","0.00028618000000000003","0.00025831555532419836","{}","5.4","3.2","2.2"); insert 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","14:47:28","crossval","0.589999968","0.26073331346959727","balloons","oc1","1","0","0.00094728","0.001090430078792168","{}","2.2","1.0","2.2");
insert 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","11:15:07","crossval","0.933611682","0.026153564189107213","breast-cancer-wisc-diag","oc1","1","0","0.49354424","0.07636875470161811","{}","22.6","11.8","6.0"); insert 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","14:47:28","crossval","0.933611682","0.026153564189107213","breast-cancer-wisc-diag","oc1","1","0","0.516594","0.07952581217903214","{}","10.8","4.2","6.0");
insert 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","11:15:07","crossval","0.703846134","0.07650658245540416","breast-cancer-wisc-prog","oc1","1","0","0.15721566","0.04308001731548455","{}","28.6","14.8","6.8"); insert 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","14:47:28","crossval","0.703846134","0.07650658245540416","breast-cancer-wisc-prog","oc1","1","0","0.15890134","0.04370013376134668","{}","13.8","5.0","6.8");
insert 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","11:15:07","crossval","0.945050076","0.015173648280147277","breast-cancer-wisc","oc1","1","0","0.25820094","0.03820597351329197","{}","31.0","16.0","7.8"); insert 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","14:47:28","crossval","0.945050076","0.015173648280147277","breast-cancer-wisc","oc1","1","0","0.2607718","0.038253786220504786","{}","15.0","5.2","7.8");
insert 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","11:15:07","crossval","0.669679358","0.07527221131701325","breast-cancer","oc1","1","0","0.1264256","0.030713546588448822","{}","61.4","31.2","10.6"); insert 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","14:47:28","crossval","0.669679358","0.07527221131701325","breast-cancer","oc1","1","0","0.12581208","0.03088834358164839","{}","30.2","7.8","10.6");
insert 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","11:15:07","crossval","0.805643748","0.020680279226445945","cardiotocography-10clases","oc1","1","0","6.03769478","0.36862710519491004","{}","311.4","156.2","15.8"); insert 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","14:47:28","crossval","0.805643748","0.020680279226445945","cardiotocography-10clases","oc1","1","0","6.26399314","0.39467220223551214","{}","155.2","50.8","15.8");
insert 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","11:15:07","crossval","0.909497716","0.013982362833392525","cardiotocography-3clases","oc1","1","0","3.27137284","0.45829975379850446","{}","134.6","67.8","11.4"); insert 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","14:47:28","crossval","0.909497716","0.013982362833392525","cardiotocography-3clases","oc1","1","0","3.40962502","0.4805920398963494","{}","66.8","21.4","11.4");
insert 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","11:15:07","crossval","0.715410214","0.0850464558161465","conn-bench-sonar-mines-rocks","oc1","1","0","0.095154","0.00918028071466227","{}","25.0","13.0","5.6"); insert 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","14:47:28","crossval","0.715410214","0.0850464558161465","conn-bench-sonar-mines-rocks","oc1","1","0","0.09973326","0.00873889882104661","{}","12.0","5.0","5.6");
insert 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","11:15:07","crossval","0.677726236","0.043664867326531236","cylinder-bands","oc1","1","0","0.51766612","0.11264194242578869","{}","94.6","47.8","11.8"); insert 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","14:47:28","crossval","0.677726236","0.043664867326531236","cylinder-bands","oc1","1","0","0.54450298","0.12061985257833803","{}","46.8","14.2","11.8");
insert 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","11:15:07","crossval","0.931162552","0.037573204772383274","dermatology","oc1","1","0","0.31285398","0.026052008314210128","{}","22.2","11.6","6.0"); insert 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","14:47:28","crossval","0.931162552","0.037573204772383274","dermatology","oc1","1","0","0.32974968","0.02642380620780729","{}","10.6","3.4","6.0");
insert 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","11:15:07","crossval","0.748119658","0.08717495251201153","echocardiogram","oc1","1","0","0.03400498","0.005967182121318201","{}","19.0","10.0","5.4"); insert 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","14:47:28","crossval","0.748119658","0.08717495251201153","echocardiogram","oc1","1","0","0.03558404","0.006167666429212982","{}","9.0","3.2","5.4");
insert 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","11:15:07","crossval","0.772","0.08339725439615099","fertility","oc1","1","0","0.0240597","0.00622999994513054","{}","14.6","7.8","5.8"); insert 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","14:47:28","crossval","0.772","0.08339725439615099","fertility","oc1","1","0","0.0246852","0.0063601546765942795","{}","6.8","1.8","5.8");
insert 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","11:15:07","crossval","0.641221574","0.07049310650191065","haberman-survival","oc1","1","0","0.0744118","0.008971614217824426","{}","70.6","35.8","10.6"); insert 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","14:47:28","crossval","0.641221574","0.07049310650191065","haberman-survival","oc1","1","0","0.07645778","0.008640722861130146","{}","34.8","9.4","10.6");
insert 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","11:15:07","crossval","0.750522798","0.06329668712180851","heart-hungarian","oc1","1","0","0.12114706","0.019238823176078317","{}","43.4","22.2","7.2"); insert 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","14:47:28","crossval","0.750522798","0.06329668712180851","heart-hungarian","oc1","1","0","0.12721674","0.01968146537684631","{}","21.2","6.2","7.2");
insert 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","11:15:07","crossval","0.779354858","0.08481264197652374","hepatitis","oc1","1","0","0.04693714","0.013074186157228967","{}","21.0","11.0","5.8"); insert 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","14:47:28","crossval","0.779354858","0.08481264197652374","hepatitis","oc1","1","0","0.04829858","0.013435143305388541","{}","10.0","3.4","5.8");
insert 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","11:15:07","crossval","0.664915948","0.049496575823232414","ilpd-indian-liver","oc1","1","0","0.26305604","0.02857327002205854","{}","117.0","59.0","15.0"); insert 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","14:47:28","crossval","0.664915948","0.049496575823232414","ilpd-indian-liver","oc1","1","0","0.2768607","0.029329177445029733","{}","58.0","16.8","15.0");
insert 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","11:15:07","crossval","0.89059155","0.03733780370201089","ionosphere","oc1","1","0","0.26317906","0.037323321091816804","{}","23.0","12.0","7.0"); insert 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","14:47:28","crossval","0.89059155","0.03733780370201089","ionosphere","oc1","1","0","0.27596064","0.039062500840429705","{}","11.0","3.0","7.0");
insert 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","11:15:07","crossval","0.949333302","0.03882632447334653","iris","oc1","1","0","0.0159865","0.003219944727944939","{}","7.4","4.2","3.2"); insert 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","14:47:28","crossval","0.949333302","0.03882632447334653","iris","oc1","1","0","0.01674434","0.003383157603753738","{}","3.2","1.0","3.2");
insert 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","11:15:07","crossval","0.7017","0.02933984544288269","led-display","oc1","1","0","0.30812904","0.017626738021076775","{}","123.0","62.0","8.0"); insert 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","14:47:28","crossval","0.7017","0.02933984544288269","led-display","oc1","1","0","0.32120998","0.01837035840305612","{}","61.0","22.8","8.0");
insert 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","11:15:07","crossval","0.633611112","0.06095436683067444","libras","oc1","1","0","0.54878306","0.0855854241711014","{}","105.8","53.4","8.6"); insert 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","14:47:28","crossval","0.633611112","0.06095436683067444","libras","oc1","1","0","0.57236222","0.08787593798548135","{}","52.4","18.4","8.6");
insert 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","11:15:07","crossval","0.832939518","0.03553219969478499","low-res-spect","oc1","1","0","1.06630056","0.19379683882082624","{}","63.0","32.0","8.0"); insert 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","14:47:28","crossval","0.832939518","0.03553219969478499","low-res-spect","oc1","1","0","1.11549722","0.2060864748626604","{}","31.0","10.2","8.0");
insert 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","11:15:07","crossval","0.730237072","0.09173834512482136","lymphography","oc1","1","0","0.0554647","0.009491613772705043","{}","18.2","9.6","4.4"); insert 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","14:47:28","crossval","0.730237072","0.09173834512482136","lymphography","oc1","1","0","0.0570192","0.009503014305890484","{}","8.6","3.4","4.4");
insert 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","11:15:07","crossval","0.772657058","0.0410826060780536","mammographic","oc1","1","0","0.34705172","0.02890701623046854","{}","179.4","90.2","14.2"); insert 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","14:47:28","crossval","0.772657058","0.0410826060780536","mammographic","oc1","1","0","0.35867934","0.028162362575267302","{}","89.2","23.0","14.2");
insert 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","11:15:07","crossval","0.73480519","0.07929681982000171","molec-biol-promoter","oc1","1","0","0.0030062","0.00023204081273670414","{}","20.6","10.8","4.8"); insert 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","14:47:28","crossval","0.73480519","0.07929681982000171","molec-biol-promoter","oc1","1","0","0.00306176","0.00022894465599835275","{}","9.8","3.4","4.8");
insert 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","11:15:07","crossval","0.76912501","0.045549030106832825","musk-1","oc1","1","0","0.65899592","0.05898071206615095","{}","59.4","30.2","11.2"); insert 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","14:47:28","crossval","0.76912501","0.045549030106832825","musk-1","oc1","1","0","0.6886358","0.062134767353564886","{}","29.2","9.8","11.2");
insert 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","11:15:07","crossval","0.725179892","0.03125153408262522","oocytes_merluccius_nucleus_4d","oc1","1","0","1.20757308","0.1573935956992416","{}","165.0","83.0","12.6"); insert 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","14:47:28","crossval","0.725179892","0.03125153408262522","oocytes_merluccius_nucleus_4d","oc1","1","0","1.27356236","0.1700721351281609","{}","82.0","26.0","12.6");
insert 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","11:15:07","crossval","0.88541974","0.022980862858349885","oocytes_merluccius_states_2f","oc1","1","0","1.03190232","0.1392155696346922","{}","66.2","33.6","7.8"); insert 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","14:47:28","crossval","0.88541974","0.022980862858349885","oocytes_merluccius_states_2f","oc1","1","0","1.08533236","0.14427176657928803","{}","32.6","10.6","7.8");
insert 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","11:15:07","crossval","0.722030574","0.03423923425578281","oocytes_trisopterus_nucleus_2f","oc1","1","0","0.74104704","0.07064859289092568","{}","141.4","71.2","12.4"); insert 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","14:47:28","crossval","0.722030574","0.03423923425578281","oocytes_trisopterus_nucleus_2f","oc1","1","0","0.77692402","0.07514951397414764","{}","70.2","22.4","12.4");
insert 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","11:15:07","crossval","0.86676659","0.02691477761893708","oocytes_trisopterus_states_5b","oc1","1","0","1.09058706","0.13758579207933633","{}","89.0","45.0","12.2"); insert 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","14:47:28","crossval","0.86676659","0.02691477761893708","oocytes_trisopterus_states_5b","oc1","1","0","1.14587276","0.1453252997908078","{}","44.0","14.8","12.2");
insert 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","11:15:07","crossval","0.846666676","0.058030588988076656","parkinsons","oc1","1","0","0.08546764","0.02077778735618342","{}","18.6","9.8","5.6"); insert 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","14:47:28","crossval","0.846666676","0.058030588988076656","parkinsons","oc1","1","0","0.089654","0.0215467307459098","{}","8.8","3.4","5.6");
insert 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","11:15:07","crossval","0.693122168","0.03596779276948685","pima","oc1","1","0","0.40212398","0.04424829635299475","{}","122.2","61.6","9.2"); insert 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","14:47:28","crossval","0.693122168","0.03596779276948685","pima","oc1","1","0","0.42004508","0.046603391710168786","{}","60.6","20.0","9.2");
insert 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","11:15:07","crossval","0.803766236","0.09698857763603047","pittsburg-bridges-MATERIAL","oc1","1","0","0.01867654","0.0037286186762742614","{}","14.2","7.6","3.8"); insert 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","14:47:28","crossval","0.803766236","0.09698857763603047","pittsburg-bridges-MATERIAL","oc1","1","0","0.01900612","0.0037102353775380196","{}","6.6","2.6","3.8");
insert 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","11:15:07","crossval","0.62873913","0.11035406374176591","pittsburg-bridges-REL-L","oc1","1","0","0.02396824","0.004048664786503001","{}","28.6","14.8","6.6"); insert 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","14:47:28","crossval","0.62873913","0.11035406374176591","pittsburg-bridges-REL-L","oc1","1","0","0.024444","0.0034031673182372867","{}","13.8","4.2","6.6");
insert 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","11:15:07","crossval","0.5948888800000001","0.1164685695698971","pittsburg-bridges-SPAN","oc1","1","0","0.0194149","0.002548420753473549","{}","25.0","13.0","5.8"); insert 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","14:47:28","crossval","0.5948888800000001","0.1164685695698971","pittsburg-bridges-SPAN","oc1","1","0","0.02011346","0.0026302542998806818","{}","12.0","4.0","5.8");
insert 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","11:15:07","crossval","0.817545458","0.07618192337367363","pittsburg-bridges-T-OR-D","oc1","1","0","0.01460786","0.004289329637029782","{}","16.2","8.6","5.4"); insert 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","14:47:28","crossval","0.817545458","0.07618192337367363","pittsburg-bridges-T-OR-D","oc1","1","0","0.01492558","0.004406823813828101","{}","7.6","2.0","5.4");
insert 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","11:15:07","crossval","0.56903508","0.09440187538222114","planning","oc1","1","0","0.0640016","0.014750071038223954","{}","41.8","21.4","8.6"); insert 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","14:47:28","crossval","0.56903508","0.09440187538222114","planning","oc1","1","0","0.06582026","0.01530466685369038","{}","20.4","6.2","8.6");
insert 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","11:15:07","crossval","0.566666656","0.11771743358490966","post-operative","oc1","1","0","0.01926424","0.004852894375398615","{}","32.2","16.6","7.2"); insert 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","14:47:28","crossval","0.566666656","0.11771743358490966","post-operative","oc1","1","0","0.0198988","0.005006215133130385","{}","15.6","4.2","7.2");
insert 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","11:15:07","crossval","0.926666678","0.036898101629179524","seeds","oc1","1","0","0.03579408","0.004055048130467524","{}","11.8","6.4","4.0"); insert 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","14:47:28","crossval","0.926666678","0.036898101629179524","seeds","oc1","1","0","0.03759648","0.004142453434714205","{}","5.4","2.2","4.0");
insert 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","11:15:07","crossval","0.587536242","0.05396622166271625","statlog-australian-credit","oc1","1","0","0.54565256","0.08693395330285902","{}","145.4","73.2","13.4"); insert 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","14:47:28","crossval","0.587536242","0.05396622166271625","statlog-australian-credit","oc1","1","0","0.57389558","0.09583586359342493","{}","72.2","20.6","13.4");
insert 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","11:15:07","crossval","0.6876","0.03162664854136749","statlog-german-credit","oc1","1","0","1.45512862","0.33086435293586547","{}","190.2","95.6","19.0"); insert 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","14:47:28","crossval","0.6876","0.03162664854136749","statlog-german-credit","oc1","1","0","1.52960272","0.3523249429756246","{}","94.6","27.8","19.0");
insert 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","11:15:07","crossval","0.73444444","0.06120324824560424","statlog-heart","oc1","1","0","0.11858528","0.020274010643004336","{}","33.8","17.4","7.0"); insert 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","14:47:28","crossval","0.73444444","0.06120324824560424","statlog-heart","oc1","1","0","0.12492442000000001","0.021020763672888496","{}","16.4","5.4","7.0");
insert 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","11:15:07","crossval","0.956709956","0.010495090136727007","statlog-image","oc1","1","0","2.76539704","0.12361159429850736","{}","103.0","52.0","12.2"); insert 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","14:47:28","crossval","0.956709956","0.010495090136727007","statlog-image","oc1","1","0","2.90438884","0.13706753941588393","{}","51.0","16.0","12.2");
insert 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","11:15:07","crossval","0.685908124","0.038501210650949656","statlog-vehicle","oc1","1","0","0.84192528","0.06554530867878762","{}","178.6","89.8","13.0"); insert 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","14:47:28","crossval","0.685908124","0.038501210650949656","statlog-vehicle","oc1","1","0","0.88143516","0.07036632176683875","{}","88.8","27.6","13.0");
insert 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","11:15:07","crossval","0.8705","0.03384849614993016","synthetic-control","oc1","1","0","1.33670054","0.10130402414297071","{}","44.2","22.6","7.2"); insert 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","14:47:28","crossval","0.8705","0.03384849614993016","synthetic-control","oc1","1","0","1.4049732","0.10596716816879787","{}","21.6","7.6","7.2");
insert 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","11:15:07","crossval","0.93214282","0.025980381693147463","tic-tac-toe","oc1","1","0","0.45076252","0.08395466496215535","{}","36.6","18.8","8.2"); insert 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","14:47:28","crossval","0.93214282","0.025980381693147463","tic-tac-toe","oc1","1","0","0.47247344","0.08710591921618374","{}","17.8","5.2","8.2");
insert 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","11:15:07","crossval","0.804838712","0.05376616787774837","vertebral-column-2clases","oc1","1","0","0.06367808","0.006029835263055853","{}","36.6","18.8","7.0"); insert 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","14:47:28","crossval","0.804838712","0.05376616787774837","vertebral-column-2clases","oc1","1","0","0.06724138","0.006135982485041388","{}","17.8","5.2","7.0");
insert 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","11:15:07","crossval","0.9045263179999999","0.055238170669175995","wine","oc1","1","0","0.03915106","0.005349230802648171","{}","9.0","5.0","3.2"); insert 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","14:47:28","crossval","0.9045263179999999","0.055238170669175995","wine","oc1","1","0","0.04064278","0.0053449924773815456","{}","4.0","1.6","3.2");
insert 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","11:15:07","crossval","0.882285718","0.08147399857032814","zoo","oc1","1","0","0.01875586","0.0020833956584854255","{}","14.2","7.6","4.0"); insert 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","14:47:28","crossval","0.882285718","0.08147399857032814","zoo","oc1","1","0","0.01932668","0.002122254081232057","{}","6.6","3.0","4.0");

157
results/normalizados/oc1.txt
View File

@@ -1,49 +1,108 @@
Processing balance-scale nodes= 40.20 leaves= 20.60 depth= 7.40 accuracy=[0.914±0.030] time=[0.172±0.023] elements=[50 50] Generating csv datasets
Processing balloons nodes= 5.40 leaves= 3.20 depth= 2.20 accuracy=[0.590±0.261] time=[0.000±0.000] elements=[50 50] ========================
Processing breast-cancer-wisc-diag nodes= 22.60 leaves= 11.80 depth= 6.00 accuracy=[0.934±0.026] time=[0.494±0.076] elements=[50 50] Generating: balance-scale, balloons, breast-cancer-wisc-diag, breast-cancer-wisc-prog, breast-cancer-wisc, breast-cancer, cardiotocography-10clases, cardiotocography-3clases, conn-bench-sonar-mines-rocks, cylinder-bands, dermatology, echocardiogram, fertility, haberman-survival, heart-hungarian, hepatitis, ilpd-indian-liver, ionosphere, iris, led-display, libras, low-res-spect, lymphography, mammographic, molec-biol-promoter, musk-1, oocytes_merluccius_nucleus_4d, oocytes_merluccius_states_2f, oocytes_trisopterus_nucleus_2f, oocytes_trisopterus_states_5b, parkinsons, pima, pittsburg-bridges-MATERIAL, pittsburg-bridges-REL-L, pittsburg-bridges-SPAN, pittsburg-bridges-T-OR-D, planning, post-operative, seeds, statlog-australian-credit, statlog-german-credit,
Processing breast-cancer-wisc-prog nodes= 28.60 leaves= 14.80 depth= 6.80 accuracy=[0.704±0.077] time=[0.157±0.043] elements=[50 50] statlog-heart, statlog-image, statlog-vehicle, synthetic-control, tic-tac-toe, vertebral-column-2clases, wine, zoo, end
Processing breast-cancer-wisc nodes= 31.00 leaves= 16.00 depth= 7.80 accuracy=[0.945±0.015] time=[0.258±0.038] elements=[50 50]
Processing breast-cancer nodes= 61.40 leaves= 31.20 depth=10.60 accuracy=[0.670±0.075] time=[0.126±0.031] elements=[50 50] Processing datasets
Processing cardiotocography-10clases nodes=311.40 leaves=156.20 depth=15.80 accuracy=[0.806±0.021] time=[6.038±0.369] elements=[50 50] ===================
Processing cardiotocography-3clases nodes=134.60 leaves= 67.80 depth=11.40 accuracy=[0.909±0.014] time=[3.271±0.458] elements=[50 50] *balance-scale : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing conn-bench-sonar-mines-rocks nodes= 25.00 leaves= 13.00 depth= 5.60 accuracy=[0.715±0.085] time=[0.095±0.009] elements=[50 50] *balloons : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing cylinder-bands nodes= 94.60 leaves= 47.80 depth=11.80 accuracy=[0.678±0.044] time=[0.518±0.113] elements=[50 50] *breast-cancer-wisc-diag : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing dermatology nodes= 22.20 leaves= 11.60 depth= 6.00 accuracy=[0.931±0.038] time=[0.313±0.026] elements=[50 50] *breast-cancer-wisc-prog : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing echocardiogram nodes= 19.00 leaves= 10.00 depth= 5.40 accuracy=[0.748±0.087] time=[0.034±0.006] elements=[50 50] *breast-cancer-wisc : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing fertility nodes= 14.60 leaves= 7.80 depth= 5.80 accuracy=[0.772±0.083] time=[0.024±0.006] elements=[50 50] *breast-cancer : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing haberman-survival nodes= 70.60 leaves= 35.80 depth=10.60 accuracy=[0.641±0.070] time=[0.074±0.009] elements=[50 50] *cardiotocography-10clases : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing heart-hungarian nodes= 43.40 leaves= 22.20 depth= 7.20 accuracy=[0.751±0.063] time=[0.121±0.019] elements=[50 50] *cardiotocography-3clases : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing hepatitis nodes= 21.00 leaves= 11.00 depth= 5.80 accuracy=[0.779±0.085] time=[0.047±0.013] elements=[50 50] *conn-bench-sonar-mines-rocks : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing ilpd-indian-liver nodes=117.00 leaves= 59.00 depth=15.00 accuracy=[0.665±0.049] time=[0.263±0.029] elements=[50 50] *cylinder-bands : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing ionosphere nodes= 23.00 leaves= 12.00 depth= 7.00 accuracy=[0.891±0.037] time=[0.263±0.037] elements=[50 50] *dermatology : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing iris nodes= 7.40 leaves= 4.20 depth= 3.20 accuracy=[0.949±0.039] time=[0.016±0.003] elements=[50 50] *echocardiogram : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing led-display nodes=123.00 leaves= 62.00 depth= 8.00 accuracy=[0.702±0.029] time=[0.308±0.018] elements=[50 50] *fertility : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing libras nodes=105.80 leaves= 53.40 depth= 8.60 accuracy=[0.634±0.061] time=[0.549±0.086] elements=[50 50] *haberman-survival : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing low-res-spect nodes= 63.00 leaves= 32.00 depth= 8.00 accuracy=[0.833±0.036] time=[1.066±0.194] elements=[50 50] *heart-hungarian : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing lymphography nodes= 18.20 leaves= 9.60 depth= 4.40 accuracy=[0.730±0.092] time=[0.055±0.009] elements=[50 50] *hepatitis : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing mammographic nodes=179.40 leaves= 90.20 depth=14.20 accuracy=[0.773±0.041] time=[0.347±0.029] elements=[50 50] *ilpd-indian-liver : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing molec-biol-promoter nodes= 20.60 leaves= 10.80 depth= 4.80 accuracy=[0.735±0.079] time=[0.003±0.000] elements=[50 50] *ionosphere : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing musk-1 nodes= 59.40 leaves= 30.20 depth=11.20 accuracy=[0.769±0.046] time=[0.659±0.059] elements=[50 50] *iris : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing oocytes_merluccius_nucleus_4d nodes=165.00 leaves= 83.00 depth=12.60 accuracy=[0.725±0.031] time=[1.208±0.157] elements=[50 50] *led-display : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing oocytes_merluccius_states_2f nodes= 66.20 leaves= 33.60 depth= 7.80 accuracy=[0.885±0.023] time=[1.032±0.139] elements=[50 50] *libras : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing oocytes_trisopterus_nucleus_2f nodes=141.40 leaves= 71.20 depth=12.40 accuracy=[0.722±0.034] time=[0.741±0.071] elements=[50 50] *low-res-spect : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing oocytes_trisopterus_states_5b nodes= 89.00 leaves= 45.00 depth=12.20 accuracy=[0.867±0.027] time=[1.091±0.138] elements=[50 50] *lymphography : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing parkinsons nodes= 18.60 leaves= 9.80 depth= 5.60 accuracy=[0.847±0.058] time=[0.085±0.021] elements=[50 50] *mammographic : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing pima nodes=122.20 leaves= 61.60 depth= 9.20 accuracy=[0.693±0.036] time=[0.402±0.044] elements=[50 50] *molec-biol-promoter : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing pittsburg-bridges-MATERIAL nodes= 14.20 leaves= 7.60 depth= 3.80 accuracy=[0.804±0.097] time=[0.019±0.004] elements=[50 50] *musk-1 : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing pittsburg-bridges-REL-L nodes= 28.60 leaves= 14.80 depth= 6.60 accuracy=[0.629±0.110] time=[0.024±0.004] elements=[50 50] *oocytes_merluccius_nucleus_4d : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing pittsburg-bridges-SPAN nodes= 25.00 leaves= 13.00 depth= 5.80 accuracy=[0.595±0.116] time=[0.019±0.003] elements=[50 50] *oocytes_merluccius_states_2f : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing pittsburg-bridges-T-OR-D nodes= 16.20 leaves= 8.60 depth= 5.40 accuracy=[0.818±0.076] time=[0.015±0.004] elements=[50 50] *oocytes_trisopterus_nucleus_2f: 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing planning nodes= 41.80 leaves= 21.40 depth= 8.60 accuracy=[0.569±0.094] time=[0.064±0.015] elements=[50 50] *oocytes_trisopterus_states_5b : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing post-operative nodes= 32.20 leaves= 16.60 depth= 7.20 accuracy=[0.567±0.118] time=[0.019±0.005] elements=[50 50] *parkinsons : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing seeds nodes= 11.80 leaves= 6.40 depth= 4.00 accuracy=[0.927±0.037] time=[0.036±0.004] elements=[50 50] *pima : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing statlog-australian-credit nodes=145.40 leaves= 73.20 depth=13.40 accuracy=[0.588±0.054] time=[0.546±0.087] elements=[50 50] *pittsburg-bridges-MATERIAL : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing statlog-german-credit nodes=190.20 leaves= 95.60 depth=19.00 accuracy=[0.688±0.032] time=[1.455±0.331] elements=[50 50] *pittsburg-bridges-REL-L : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing statlog-heart nodes= 33.80 leaves= 17.40 depth= 7.00 accuracy=[0.734±0.061] time=[0.119±0.020] elements=[50 50] *pittsburg-bridges-SPAN : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing statlog-image nodes=103.00 leaves= 52.00 depth=12.20 accuracy=[0.957±0.010] time=[2.765±0.124] elements=[50 50] *pittsburg-bridges-T-OR-D : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing statlog-vehicle nodes=178.60 leaves= 89.80 depth=13.00 accuracy=[0.686±0.039] time=[0.842±0.066] elements=[50 50] *planning : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing synthetic-control nodes= 44.20 leaves= 22.60 depth= 7.20 accuracy=[0.871±0.034] time=[1.337±0.101] elements=[50 50] *post-operative : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing tic-tac-toe nodes= 36.60 leaves= 18.80 depth= 8.20 accuracy=[0.932±0.026] time=[0.451±0.084] elements=[50 50] *seeds : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing vertebral-column-2clases nodes= 36.60 leaves= 18.80 depth= 7.00 accuracy=[0.805±0.054] time=[0.064±0.006] elements=[50 50] *statlog-australian-credit : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing wine nodes= 9.00 leaves= 5.00 depth= 3.20 accuracy=[0.905±0.055] time=[0.039±0.005] elements=[50 50] *statlog-german-credit : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Processing zoo nodes= 14.20 leaves= 7.60 depth= 4.00 accuracy=[0.882±0.081] time=[0.019±0.002] elements=[50 50] *statlog-heart : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*statlog-image : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*statlog-vehicle : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*synthetic-control : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*tic-tac-toe : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*vertebral-column-2clases : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*wine : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
*zoo : 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1, done
Generating SQL
==============
Processing balance-scale nodes= 19.60 leaves= 6.60 depth= 7.40 accuracy=[0.914±0.030] time=[0.178±0.023] elements=[50 50]
Processing balloons nodes= 2.20 leaves= 1.00 depth= 2.20 accuracy=[0.590±0.261] time=[0.001±0.001] elements=[50 50]
Processing breast-cancer-wisc-diag nodes= 10.80 leaves= 4.20 depth= 6.00 accuracy=[0.934±0.026] time=[0.517±0.080] elements=[50 50]
Processing breast-cancer-wisc-prog nodes= 13.80 leaves= 5.00 depth= 6.80 accuracy=[0.704±0.077] time=[0.159±0.044] elements=[50 50]
Processing breast-cancer-wisc nodes= 15.00 leaves= 5.20 depth= 7.80 accuracy=[0.945±0.015] time=[0.261±0.038] elements=[50 50]
Processing breast-cancer nodes= 30.20 leaves= 7.80 depth=10.60 accuracy=[0.670±0.075] time=[0.126±0.031] elements=[50 50]
Processing cardiotocography-10clases nodes=155.20 leaves= 50.80 depth=15.80 accuracy=[0.806±0.021] time=[6.264±0.395] elements=[50 50]
Processing cardiotocography-3clases nodes= 66.80 leaves= 21.40 depth=11.40 accuracy=[0.909±0.014] time=[3.410±0.481] elements=[50 50]
Processing conn-bench-sonar-mines-rocks nodes= 12.00 leaves= 5.00 depth= 5.60 accuracy=[0.715±0.085] time=[0.100±0.009] elements=[50 50]
Processing cylinder-bands nodes= 46.80 leaves= 14.20 depth=11.80 accuracy=[0.678±0.044] time=[0.545±0.121] elements=[50 50]
Processing dermatology nodes= 10.60 leaves= 3.40 depth= 6.00 accuracy=[0.931±0.038] time=[0.330±0.026] elements=[50 50]
Processing echocardiogram nodes= 9.00 leaves= 3.20 depth= 5.40 accuracy=[0.748±0.087] time=[0.036±0.006] elements=[50 50]
Processing fertility nodes= 6.80 leaves= 1.80 depth= 5.80 accuracy=[0.772±0.083] time=[0.025±0.006] elements=[50 50]
Processing haberman-survival nodes= 34.80 leaves= 9.40 depth=10.60 accuracy=[0.641±0.070] time=[0.076±0.009] elements=[50 50]
Processing heart-hungarian nodes= 21.20 leaves= 6.20 depth= 7.20 accuracy=[0.751±0.063] time=[0.127±0.020] elements=[50 50]
Processing hepatitis nodes= 10.00 leaves= 3.40 depth= 5.80 accuracy=[0.779±0.085] time=[0.048±0.013] elements=[50 50]
Processing ilpd-indian-liver nodes= 58.00 leaves= 16.80 depth=15.00 accuracy=[0.665±0.049] time=[0.277±0.029] elements=[50 50]
Processing ionosphere nodes= 11.00 leaves= 3.00 depth= 7.00 accuracy=[0.891±0.037] time=[0.276±0.039] elements=[50 50]
Processing iris nodes= 3.20 leaves= 1.00 depth= 3.20 accuracy=[0.949±0.039] time=[0.017±0.003] elements=[50 50]
Processing led-display nodes= 61.00 leaves= 22.80 depth= 8.00 accuracy=[0.702±0.029] time=[0.321±0.018] elements=[50 50]
Processing libras nodes= 52.40 leaves= 18.40 depth= 8.60 accuracy=[0.634±0.061] time=[0.572±0.088] elements=[50 50]
Processing low-res-spect nodes= 31.00 leaves= 10.20 depth= 8.00 accuracy=[0.833±0.036] time=[1.115±0.206] elements=[50 50]
Processing lymphography nodes= 8.60 leaves= 3.40 depth= 4.40 accuracy=[0.730±0.092] time=[0.057±0.010] elements=[50 50]
Processing mammographic nodes= 89.20 leaves= 23.00 depth=14.20 accuracy=[0.773±0.041] time=[0.359±0.028] elements=[50 50]
Processing molec-biol-promoter nodes= 9.80 leaves= 3.40 depth= 4.80 accuracy=[0.735±0.079] time=[0.003±0.000] elements=[50 50]
Processing musk-1 nodes= 29.20 leaves= 9.80 depth=11.20 accuracy=[0.769±0.046] time=[0.689±0.062] elements=[50 50]
Processing oocytes_merluccius_nucleus_4d nodes= 82.00 leaves= 26.00 depth=12.60 accuracy=[0.725±0.031] time=[1.274±0.170] elements=[50 50]
Processing oocytes_merluccius_states_2f nodes= 32.60 leaves= 10.60 depth= 7.80 accuracy=[0.885±0.023] time=[1.085±0.144] elements=[50 50]
Processing oocytes_trisopterus_nucleus_2f nodes= 70.20 leaves= 22.40 depth=12.40 accuracy=[0.722±0.034] time=[0.777±0.075] elements=[50 50]
Processing oocytes_trisopterus_states_5b nodes= 44.00 leaves= 14.80 depth=12.20 accuracy=[0.867±0.027] time=[1.146±0.145] elements=[50 50]
Processing parkinsons nodes= 8.80 leaves= 3.40 depth= 5.60 accuracy=[0.847±0.058] time=[0.090±0.022] elements=[50 50]
Processing pima nodes= 60.60 leaves= 20.00 depth= 9.20 accuracy=[0.693±0.036] time=[0.420±0.047] elements=[50 50]
Processing pittsburg-bridges-MATERIAL nodes= 6.60 leaves= 2.60 depth= 3.80 accuracy=[0.804±0.097] time=[0.019±0.004] elements=[50 50]
Processing pittsburg-bridges-REL-L nodes= 13.80 leaves= 4.20 depth= 6.60 accuracy=[0.629±0.110] time=[0.024±0.003] elements=[50 50]
Processing pittsburg-bridges-SPAN nodes= 12.00 leaves= 4.00 depth= 5.80 accuracy=[0.595±0.116] time=[0.020±0.003] elements=[50 50]
Processing pittsburg-bridges-T-OR-D nodes= 7.60 leaves= 2.00 depth= 5.40 accuracy=[0.818±0.076] time=[0.015±0.004] elements=[50 50]
Processing planning nodes= 20.40 leaves= 6.20 depth= 8.60 accuracy=[0.569±0.094] time=[0.066±0.015] elements=[50 50]
Processing post-operative nodes= 15.60 leaves= 4.20 depth= 7.20 accuracy=[0.567±0.118] time=[0.020±0.005] elements=[50 50]
Processing seeds nodes= 5.40 leaves= 2.20 depth= 4.00 accuracy=[0.927±0.037] time=[0.038±0.004] elements=[50 50]
Processing statlog-australian-credit nodes= 72.20 leaves= 20.60 depth=13.40 accuracy=[0.588±0.054] time=[0.574±0.096] elements=[50 50]
Processing statlog-german-credit nodes= 94.60 leaves= 27.80 depth=19.00 accuracy=[0.688±0.032] time=[1.530±0.352] elements=[50 50]
Processing statlog-heart nodes= 16.40 leaves= 5.40 depth= 7.00 accuracy=[0.734±0.061] time=[0.125±0.021] elements=[50 50]
Processing statlog-image nodes= 51.00 leaves= 16.00 depth=12.20 accuracy=[0.957±0.010] time=[2.904±0.137] elements=[50 50]
Processing statlog-vehicle nodes= 88.80 leaves= 27.60 depth=13.00 accuracy=[0.686±0.039] time=[0.881±0.070] elements=[50 50]
Processing synthetic-control nodes= 21.60 leaves= 7.60 depth= 7.20 accuracy=[0.871±0.034] time=[1.405±0.106] elements=[50 50]
Processing tic-tac-toe nodes= 17.80 leaves= 5.20 depth= 8.20 accuracy=[0.932±0.026] time=[0.472±0.087] elements=[50 50]
Processing vertebral-column-2clases nodes= 17.80 leaves= 5.20 depth= 7.00 accuracy=[0.805±0.054] time=[0.067±0.006] elements=[50 50]
Processing wine nodes= 4.00 leaves= 1.60 depth= 3.20 accuracy=[0.905±0.055] time=[0.041±0.005] elements=[50 50]
Processing zoo nodes= 6.60 leaves= 3.00 depth= 4.00 accuracy=[0.882±0.081] time=[0.019±0.002] elements=[50 50]