Merge branch 'main' of github.com:doctorado-ml/benchmark into main

results/exreport_accuracy.csv

@@ -1,638 +0,0 @@
-classifier, dataset, accuracy, stdev
-ExtraTree, balance-scale, 0.7878399999999999, 0.03687783073880566
-ExtraTree, balloons, 0.575, 0.28927591596182967
-ExtraTree, breast-cancer-wisc-diag, 0.9131858407079645, 0.027969722048511687
-ExtraTree, breast-cancer-wisc-prog, 0.6632179487179486, 0.07656410793102837
-ExtraTree, breast-cancer-wisc, 0.9403360739979445, 0.02165140889671454
-ExtraTree, breast-cancer, 0.656908650937689, 0.05914309228326181
-ExtraTree, cardiotocography-10clases, 0.6989177575255454, 0.030051929270415455
-ExtraTree, cardiotocography-3clases, 0.8819887323943664, 0.01507833257138732
-ExtraTree, conn-bench-sonar-mines-rocks, 0.6956445993031357, 0.07653320217222026
-ExtraTree, cylinder-bands, 0.6958918713116314, 0.04592870865503856
-ExtraTree, dermatology, 0.8975268419104035, 0.039527668904658364
-ExtraTree, echocardiogram, 0.7305982905982905, 0.08853188223988526
-ExtraTree, fertility, 0.8059999999999999, 0.06902173570694958
-ExtraTree, haberman-survival, 0.6500105764145954, 0.06301971546791206
-ExtraTree, heart-hungarian, 0.7604909409701931, 0.04869532741159298
-ExtraTree, hepatitis, 0.7793548387096775, 0.07805118521205993
-ExtraTree, ilpd-indian-liver, 0.6634232242852933, 0.03869963692900845
-ExtraTree, ionosphere, 0.8540885311871226, 0.04332727262995767
-ExtraTree, iris, 0.9333333333333335, 0.044221663871405324
-ExtraTree, led-display, 0.7036999999999999, 0.02880642289490313
-ExtraTree, libras, 0.6022222222222223, 0.05851558829982036
-ExtraTree, low-res-spect, 0.8043255157820492, 0.03860708555393554
-ExtraTree, lymphography, 0.7288505747126437, 0.07209829097764534
-ExtraTree, mammographic, 0.7595207253886009, 0.027055981087449776
-ExtraTree, molec-biol-promoter, 0.6611255411255411, 0.09268635844993099
-ExtraTree, musk-1, 0.7796030701754388, 0.043688902783382194
-ExtraTree, oocytes_merluccius_nucleus_4d, 0.6907929220468675, 0.025573299489788766
-ExtraTree, oocytes_merluccius_states_2f, 0.8678149210903873, 0.01875101003253329
-ExtraTree, oocytes_trisopterus_nucleus_2f, 0.6820134510298443, 0.035675489219353006
-ExtraTree, oocytes_trisopterus_states_5b, 0.8403422806701495, 0.02618600526924095
-ExtraTree, parkinsons, 0.8482051282051282, 0.06020783639564789
-ExtraTree, pima, 0.6649944826415415, 0.03466246243736563
-ExtraTree, pittsburg-bridges-MATERIAL, 0.7835930735930735, 0.07970952540707685
-ExtraTree, pittsburg-bridges-REL-L, 0.6253333333333333, 0.10873604896570042
-ExtraTree, pittsburg-bridges-SPAN, 0.5715204678362573, 0.12252433352049193
-ExtraTree, pittsburg-bridges-T-OR-D, 0.8365714285714287, 0.08634786997098468
-ExtraTree, planning, 0.5972672672672672, 0.08066489784697915
-ExtraTree, post-operative, 0.5733333333333334, 0.10566660824861103
-ExtraTree, seeds, 0.8838095238095236, 0.04354995567685211
-ExtraTree, statlog-australian-credit, 0.5704347826086956, 0.041879795179494514
-ExtraTree, statlog-german-credit, 0.6663999999999999, 0.030692670134740637
-ExtraTree, statlog-heart, 0.7507407407407407, 0.04860326215293292
-ExtraTree, statlog-image, 0.928917748917749, 0.011578447915777758
-ExtraTree, statlog-vehicle, 0.6626481030281935, 0.029721786236711036
-ExtraTree, synthetic-control, 0.8525, 0.03449033681095813
-ExtraTree, tic-tac-toe, 0.8658529668411867, 0.030146489305175026
-ExtraTree, vertebral-column-2clases, 0.747741935483871, 0.06311049392365933
-ExtraTree, wine, 0.8719999999999999, 0.06055135103670992
-ExtraTree, zoo, 0.9346666666666665, 0.06150449350322653
-STree, balance-scale, 0.97056, 0.015046806970251203
-STree, balloons, 0.86, 0.28501461950807594
-STree, breast-cancer-wisc-diag, 0.9727635460332246, 0.017313211324042233
-STree, breast-cancer-wisc-prog, 0.811128205128205, 0.05846009894763935
-STree, breast-cancer-wisc, 0.967808838643371, 0.012084225264206064
-STree, breast-cancer, 0.7342105263157894, 0.047977358056609264
-STree, cardiotocography-10clases, 0.8094129798398231, 0.023346080031830214
-STree, cardiotocography-3clases, 0.904047500690417, 0.015332156149647586
-STree, conn-bench-sonar-mines-rocks, 0.8320905923344947, 0.06095204566527709
-STree, cylinder-bands, 0.7476127926898914, 0.04110007272505122
-STree, dermatology, 0.9729211403184006, 0.019813499795909784
-STree, echocardiogram, 0.855156695156695, 0.06266151037590971
-STree, fertility, 0.88, 0.0547722557505166
-STree, haberman-survival, 0.735637228979376, 0.04346136548997221
-STree, heart-hungarian, 0.8275219170075979, 0.05052827428335672
-STree, hepatitis, 0.8245161290322581, 0.073887165430815
-STree, ilpd-indian-liver, 0.7234983790156204, 0.038488555090414656
-STree, ionosphere, 0.9532756539235413, 0.02385368651558141
-STree, iris, 0.966, 0.032991581417756315
-STree, led-display, 0.7030000000000001, 0.029120439557122058
-STree, libras, 0.7886111111111112, 0.05169130237032862
-STree, low-res-spect, 0.8975365896667254, 0.03121679591619338
-STree, lymphography, 0.8350344827586206, 0.05906491141171708
-STree, mammographic, 0.8267465457685665, 0.022926835290767923
-STree, molec-biol-promoter, 0.827142857142857, 0.09431966640910128
-STree, musk-1, 0.9163881578947368, 0.027520787090975468
-STree, oocytes_merluccius_nucleus_4d, 0.8351252989000477, 0.02209607725442717
-STree, oocytes_merluccius_states_2f, 0.9179062649450025, 0.016316580296653664
-STree, oocytes_trisopterus_nucleus_2f, 0.8009860085269921, 0.0218449453461112
-STree, oocytes_trisopterus_states_5b, 0.9224704257491143, 0.01772207930954525
-STree, parkinsons, 0.882051282051282, 0.04783271309276316
-STree, pima, 0.7692530345471521, 0.02618732989053553
-STree, pittsburg-bridges-MATERIAL, 0.8677489177489177, 0.07122264688853039
-STree, pittsburg-bridges-REL-L, 0.6672857142857144, 0.09562399199889633
-STree, pittsburg-bridges-SPAN, 0.6794736842105265, 0.10621707149419587
-STree, pittsburg-bridges-T-OR-D, 0.8628095238095238, 0.0747571882042698
-STree, planning, 0.7352702702702704, 0.06697755238925641
-STree, post-operative, 0.711111111111111, 0.07535922203472521
-STree, seeds, 0.9528571428571427, 0.0279658035429067
-STree, statlog-australian-credit, 0.6782608695652174, 0.03904983647915211
-STree, statlog-german-credit, 0.7644, 0.028803472012936236
-STree, statlog-heart, 0.8229629629629629, 0.044003990341567836
-STree, statlog-image, 0.9559307359307361, 0.00956073126474503
-STree, statlog-vehicle, 0.7930281935259312, 0.03010396812322711
-STree, synthetic-control, 0.9833333333333334, 0.01092906420717001
-STree, tic-tac-toe, 0.9844442626527051, 0.008387465200358178
-STree, vertebral-column-2clases, 0.8529032258064515, 0.04088510843291576
-STree, wine, 0.9791587301587302, 0.022426953738041516
-STree, zoo, 0.9575238095238094, 0.04546150348723332
-ODTE, balance-scale, 0.97376, 0.019001642034308507
-ODTE, balloons, 0.7583333333333333, 0.2785129161178067
-ODTE, breast-cancer-wisc-diag, 0.9739931687626143, 0.016554866765950735
-ODTE, breast-cancer-wisc-prog, 0.810102564102564, 0.06056448251036948
-ODTE, breast-cancer-wisc, 0.971956834532374, 0.01262594441445573
-ODTE, breast-cancer, 0.7433877797943134, 0.04272794739105822
-ODTE, cardiotocography-10clases, 0.8377754211543773, 0.020035258312140675
-ODTE, cardiotocography-3clases, 0.9158537420602043, 0.013571959275117652
-ODTE, conn-bench-sonar-mines-rocks, 0.8431475029036002, 0.052081786915494255
-ODTE, cylinder-bands, 0.7673272415762422, 0.041472855015354505
-ODTE, dermatology, 0.9805997778600516, 0.012861009889409938
-ODTE, echocardiogram, 0.855156695156695, 0.06266151037590971
-ODTE, fertility, 0.88, 0.0547722557505166
-ODTE, haberman-survival, 0.7408778424114226, 0.05131929321866528
-ODTE, heart-hungarian, 0.8299123319696085, 0.05556757760909393
-ODTE, hepatitis, 0.8283870967741936, 0.07109665710428856
-ODTE, ilpd-indian-liver, 0.7272885352195698, 0.039873431660598986
-ODTE, ionosphere, 0.9504346076458754, 0.023708279806973313
-ODTE, iris, 0.964, 0.03255081497528987
-ODTE, led-display, 0.7186, 0.02703775138579389
-ODTE, libras, 0.8625, 0.047079031268550814
-ODTE, low-res-spect, 0.9093969317580672, 0.035428439542986974
-ODTE, lymphography, 0.8410574712643678, 0.05833785272114171
-ODTE, mammographic, 0.8288347366148531, 0.022955531218077037
-ODTE, molec-biol-promoter, 0.8656709956709955, 0.0973712407439469
-ODTE, musk-1, 0.9124013157894737, 0.02990026230299551
-ODTE, oocytes_merluccius_nucleus_4d, 0.8443271162123385, 0.026958181251392942
-ODTE, oocytes_merluccius_states_2f, 0.9230946915351506, 0.015908353110954166
-ODTE, oocytes_trisopterus_nucleus_2f, 0.8154464661022038, 0.022327974942331383
-ODTE, oocytes_trisopterus_states_5b, 0.9267387257551191, 0.01807476944375977
-ODTE, parkinsons, 0.9066666666666667, 0.04522134909753559
-ODTE, pima, 0.7728910958322721, 0.02900120258111359
-ODTE, pittsburg-bridges-MATERIAL, 0.8677489177489177, 0.06934417716014449
-ODTE, pittsburg-bridges-REL-L, 0.6966190476190477, 0.099521633605742
-ODTE, pittsburg-bridges-SPAN, 0.6937426900584795, 0.10948613227073402
-ODTE, pittsburg-bridges-T-OR-D, 0.8664761904761904, 0.07649172587269897
-ODTE, planning, 0.7352702702702704, 0.06697755238925641
-ODTE, post-operative, 0.711111111111111, 0.07535922203472521
-ODTE, seeds, 0.9571428571428572, 0.027355060221609648
-ODTE, statlog-australian-credit, 0.6782608695652174, 0.03904983647915211
-ODTE, statlog-german-credit, 0.7576, 0.029227384419410526
-ODTE, statlog-heart, 0.8381481481481482, 0.046379185057354784
-ODTE, statlog-image, 0.9641991341991343, 0.007272340744212073
-ODTE, statlog-vehicle, 0.8072182387747998, 0.02404945671905629
-ODTE, synthetic-control, 0.987, 0.009741092797468319
-ODTE, tic-tac-toe, 0.9871591404886563, 0.0074727077754232615
-ODTE, vertebral-column-2clases, 0.8564516129032256, 0.037928954891305953
-ODTE, wine, 0.9814285714285714, 0.02424781771927406
-ODTE, zoo, 0.9574761904761906, 0.04557023434769017
-TBRoF, balance-scale, 0.7826380042462844, 0.18925694969056328
-TBRoF, balloons, 0.61125, 0.247725203364352
-TBRoF, breast-cancer-wisc-diag, 0.973283758495026, 0.013244810623357882
-TBRoF, breast-cancer-wisc-prog, 0.7978151260504203, 0.05694766463433709
-TBRoF, breast-cancer-wisc, 0.9627849210987725, 0.012721293409234216
-TBRoF, breast-cancer, 0.7308990931892726, 0.055966571642361095
-TBRoF, cardiotocography-10clases, 0.7833492331011966, 0.021307069831893954
-TBRoF, cardiotocography-3clases, 0.895985255615268, 0.012308158973203483
-TBRoF, conn-bench-sonar-mines-rocks, 0.7877884615384616, 0.04851951284665777
-TBRoF, cylinder-bands, 0.7170703125, 0.035382373191454965
-TBRoF, dermatology, 0.9742673992673991, 0.015317005568040842
-TBRoF, echocardiogram, 0.8331428571428572, 0.05716070028311154
-TBRoF, fertility, 0.8800000000000006, 0.05570922354457741
-TBRoF, haberman-survival, 0.7412618083670716, 0.03733056862694105
-TBRoF, heart-hungarian, 0.814958904109589, 0.03136114741864325
-TBRoF, hepatitis, 0.8127086007702183, 0.05401110717213335
-TBRoF, ilpd-indian-liver, 0.7232548928238585, 0.02812722020593907
-TBRoF, ionosphere, 0.907727969348659, 0.03151367661074491
-TBRoF, iris, 0.9774601524601525, 0.022227864533454735
-TBRoF, led-display, 0.7098000000000001, 0.026706198767918625
-TBRoF, libras, 0.7562777777777778, 0.04860581848829984
-TBRoF, low-res-spect, 0.8738299663299666, 0.029720937467041728
-TBRoF, lymphography, 0.7751351351351352, 0.06933105447844684
-TBRoF, mammographic, 0.8145772821576763, 0.023864461511594333
-TBRoF, molec-biol-promoter, 0.7795329670329672, 0.06980216133544012
-TBRoF, musk-1, 0.8714285714285717, 0.02544487451716707
-TBRoF, oocytes_merluccius_nucleus_4d, 0.831322194247349, 0.021134901304659903
-TBRoF, oocytes_merluccius_states_2f, 0.9211329823758299, 0.012866634130284658
-TBRoF, oocytes_trisopterus_nucleus_2f, 0.8083333333333332, 0.029987740012144073
-TBRoF, oocytes_trisopterus_states_5b, 0.9314473684210521, 0.015009780777726503
-TBRoF, parkinsons, 0.8935600490196077, 0.04414473458115928
-TBRoF, pima, 0.7682291666666667, 0.02576538959165189
-TBRoF, pittsburg-bridges-MATERIAL, 0.8053021978021977, 0.06860551894291693
-TBRoF, pittsburg-bridges-REL-L, 0.6569285714285714, 0.0689374239513824
-TBRoF, pittsburg-bridges-SPAN, 0.6478260869565218, 0.10917514377689
-TBRoF, pittsburg-bridges-T-OR-D, 0.8656444444444447, 0.0655148575959644
-TBRoF, planning, 0.7128439716312058, 0.05752795189294039
-TBRoF, post-operative, 0.7110227272727273, 0.08718498768060559
-TBRoF, seeds, 0.9534900284900283, 0.027705208350106637
-TBRoF, statlog-australian-credit, 0.6784883720930234, 0.03892040204721194
-TBRoF, statlog-german-credit, 0.7575999999999999, 0.0256092088700016
-TBRoF, statlog-heart, 0.8289746917585983, 0.042595090023633556
-TBRoF, statlog-image, 0.9615544640104406, 0.008699636358413849
-TBRoF, statlog-vehicle, 0.8051545290701557, 0.02813895748743846
-TBRoF, synthetic-control, 0.9816666666666667, 0.012955163345552294
-TBRoF, tic-tac-toe, 0.983303703189291, 0.008126990928514651
-TBRoF, vertebral-column-2clases, 0.8501578168666777, 0.03368363428991826
-TBRoF, wine, 0.9910573122529645, 0.013443915807807105
-TBRoF, zoo, 0.9566153846153845, 0.04584946867346845
-Wodt, balance-scale, 0.92128, 0.031368162203100125
-Wodt, balloons, 0.6849999999999998, 0.23879907872519107
-Wodt, breast-cancer-wisc-diag, 0.9662598975314391, 0.012701511719760309
-Wodt, breast-cancer-wisc-prog, 0.7116025641025641, 0.054983897290113326
-Wodt, breast-cancer-wisc, 0.9440647482014388, 0.01756316459913997
-Wodt, breast-cancer, 0.6632486388384754, 0.04925109121345335
-Wodt, cardiotocography-10clases, 0.7796334714167358, 0.01966283976162998
-Wodt, cardiotocography-3clases, 0.9014120961060482, 0.016029994123889068
-Wodt, conn-bench-sonar-mines-rocks, 0.8081997677119628, 0.0498734154479995
-Wodt, cylinder-bands, 0.7025452122596612, 0.03710659493721448
-Wodt, dermatology, 0.9641873380229546, 0.016641323865204168
-Wodt, echocardiogram, 0.746980056980057, 0.08724923657104314
-Wodt, fertility, 0.804, 0.06770524351924304
-Wodt, haberman-survival, 0.6702379693283977, 0.05088784373091166
-Wodt, heart-hungarian, 0.761928696668615, 0.052100433118643194
-Wodt, hepatitis, 0.7748387096774192, 0.09558190259697842
-Wodt, ilpd-indian-liver, 0.6778588269967581, 0.035631066332769044
-Wodt, ionosphere, 0.8794607645875252, 0.03858080219956032
-Wodt, iris, 0.9466666666666668, 0.036514837167011066
-Wodt, led-display, 0.7047, 0.02916179006851258
-Wodt, libras, 0.7855555555555556, 0.05031996388544112
-Wodt, low-res-spect, 0.855533415623347, 0.03222774975371431
-Wodt, lymphography, 0.7800459770114943, 0.07599005574288029
-Wodt, mammographic, 0.7579566062176167, 0.024906146679797566
-Wodt, molec-biol-promoter, 0.7912987012987013, 0.08633959022510546
-Wodt, musk-1, 0.8411776315789474, 0.03449014306500045
-Wodt, oocytes_merluccius_nucleus_4d, 0.74041893830703, 0.028693112692103676
-Wodt, oocytes_merluccius_states_2f, 0.902934481109517, 0.022147306742109327
-Wodt, oocytes_trisopterus_nucleus_2f, 0.7491436978322225, 0.03602040782195508
-Wodt, oocytes_trisopterus_states_5b, 0.8916705698672911, 0.020702811014803944
-Wodt, parkinsons, 0.9005128205128206, 0.05144589211891057
-Wodt, pima, 0.6881622952211188, 0.035111562938444044
-Wodt, pittsburg-bridges-MATERIAL, 0.8118614718614716, 0.08062163607707493
-Wodt, pittsburg-bridges-REL-L, 0.6259047619047619, 0.10926121729800642
-Wodt, pittsburg-bridges-SPAN, 0.5871929824561403, 0.11611933933595711
-Wodt, pittsburg-bridges-T-OR-D, 0.8375238095238096, 0.07413072965623015
-Wodt, planning, 0.5784384384384385, 0.08440752357130758
-Wodt, post-operative, 0.5566666666666666, 0.10627822791331752
-Wodt, seeds, 0.9323809523809523, 0.04161152814276852
-Wodt, statlog-australian-credit, 0.5617391304347826, 0.03785467296768582
-Wodt, statlog-german-credit, 0.6850999999999999, 0.02579709285946769
-Wodt, statlog-heart, 0.78, 0.061154866490519384
-Wodt, statlog-image, 0.9548917748917749, 0.011022906583856079
-Wodt, statlog-vehicle, 0.7296756004176818, 0.03014045315193517
-Wodt, synthetic-control, 0.9785, 0.01453826368969525
-Wodt, tic-tac-toe, 0.9449798211169285, 0.029347280139619656
-Wodt, vertebral-column-2clases, 0.8138709677419353, 0.04530047675895885
-Wodt, wine, 0.967920634920635, 0.028208046326376822
-Wodt, zoo, 0.9514285714285715, 0.043724036675722826
-RandomForest, balance-scale, 0.8488000000000001, 0.025562472493872744
-RandomForest, balloons, 0.6583333333333333, 0.24958298553119895
-RandomForest, breast-cancer-wisc-diag, 0.9602856699270299, 0.01304850246785924
-RandomForest, breast-cancer-wisc-prog, 0.7889487179487179, 0.062095973522275645
-RandomForest, breast-cancer-wisc, 0.9682384378211717, 0.012979760074633098
-RandomForest, breast-cancer, 0.7173260738052026, 0.05543339031060783
-RandomForest, cardiotocography-10clases, 0.8729086992543498, 0.01716119423915263
-RandomForest, cardiotocography-3clases, 0.9415357083678542, 0.011745284632829643
-RandomForest, conn-bench-sonar-mines-rocks, 0.833437862950058, 0.05787314265134896
-RandomForest, cylinder-bands, 0.792147344374643, 0.03975693903506292
-RandomForest, dermatology, 0.9778563495001852, 0.015194540700548658
-RandomForest, echocardiogram, 0.8292592592592591, 0.07125299167854407
-RandomForest, fertility, 0.887, 0.06067124524847005
-RandomForest, haberman-survival, 0.7084822845055527, 0.05577963957846104
-RandomForest, heart-hungarian, 0.8169783752191703, 0.047084407169907465
-RandomForest, hepatitis, 0.8225806451612903, 0.07162452681024038
-RandomForest, ilpd-indian-liver, 0.7048010610079575, 0.03367965359465586
-RandomForest, ionosphere, 0.9327565392354125, 0.02623998580282412
-RandomForest, iris, 0.9499999999999998, 0.03144660377352201
-RandomForest, led-display, 0.7132000000000001, 0.027545598559479512
-RandomForest, libras, 0.8008333333333334, 0.051039351799359346
-RandomForest, low-res-spect, 0.8941421266090636, 0.033512494391602414
-RandomForest, lymphography, 0.8472873563218392, 0.07011193134668385
-RandomForest, mammographic, 0.8082259283246978, 0.022691310103387825
-RandomForest, molec-biol-promoter, 0.8914718614718615, 0.06923296108787423
-RandomForest, musk-1, 0.8892916666666667, 0.032218163597528444
-RandomForest, oocytes_merluccius_nucleus_4d, 0.8008857006217122, 0.026005202449345177
-RandomForest, oocytes_merluccius_states_2f, 0.9224098517455765, 0.014089731590980637
-RandomForest, oocytes_trisopterus_nucleus_2f, 0.80932324506095, 0.02940827813910382
-RandomForest, oocytes_trisopterus_states_5b, 0.9135879421125322, 0.018448127398280962
-RandomForest, parkinsons, 0.906153846153846, 0.043092182889324544
-RandomForest, pima, 0.7619658772599949, 0.029215416907967737
-RandomForest, pittsburg-bridges-MATERIAL, 0.8393939393939392, 0.06465451660842308
-RandomForest, pittsburg-bridges-REL-L, 0.6780476190476192, 0.08371922895638823
-RandomForest, pittsburg-bridges-SPAN, 0.6433333333333333, 0.1000367396003129
-RandomForest, pittsburg-bridges-T-OR-D, 0.856095238095238, 0.07211284933039848
-RandomForest, planning, 0.7022522522522522, 0.06398440367639394
-RandomForest, post-operative, 0.6344444444444445, 0.08613082211681551
-RandomForest, seeds, 0.9333333333333332, 0.041785544701867246
-RandomForest, statlog-australian-credit, 0.6682608695652174, 0.03491189739487307
-RandomForest, statlog-german-credit, 0.7622999999999999, 0.02486382914999218
-RandomForest, statlog-heart, 0.8307407407407408, 0.044445987627530076
-RandomForest, statlog-image, 0.9785714285714286, 0.007182103906076649
-RandomForest, statlog-vehicle, 0.7494166376609818, 0.02747614895571888
-RandomForest, synthetic-control, 0.9860000000000001, 0.010598742063723111
-RandomForest, tic-tac-toe, 0.9884075043630017, 0.0071140588943835546
-RandomForest, vertebral-column-2clases, 0.8322580645161289, 0.04539112025635577
-RandomForest, wine, 0.9825873015873016, 0.02303907423919074
-RandomForest, zoo, 0.9594761904761906, 0.04292716501935155
-BaggingStree, balance-scale, 0.9743999999999999, 0.01893145530591877
-BaggingStree, balloons, 0.7733333333333333, 0.27235597621087326
-BaggingStree, breast-cancer-wisc-diag, 0.9759276509858718, 0.014451052166715531
-BaggingStree, breast-cancer-wisc-prog, 0.8091410256410256, 0.05525623326760874
-BaggingStree, breast-cancer-wisc, 0.9682353545734841, 0.013372407783798355
-BaggingStree, breast-cancer, 0.7394494857834241, 0.04747649879695565
-BaggingStree, cardiotocography-10clases, 0.8287906103286384, 0.019919444360845064
-BaggingStree, cardiotocography-3clases, 0.9121377520022094, 0.01484624306368222
-BaggingStree, conn-bench-sonar-mines-rocks, 0.8364227642276423, 0.05768447229690234
-BaggingStree, cylinder-bands, 0.7509347039786789, 0.041363431918714964
-BaggingStree, dermatology, 0.983054424287301, 0.01146631014778772
-BaggingStree, echocardiogram, 0.855156695156695, 0.06266151037590971
-BaggingStree, fertility, 0.88, 0.0547722557505166
-BaggingStree, haberman-survival, 0.7359545214172395, 0.047857454758329004
-BaggingStree, heart-hungarian, 0.8299006428988897, 0.050874048012859115
-BaggingStree, hepatitis, 0.8283870967741934, 0.06780028209248852
-BaggingStree, ilpd-indian-liver, 0.7269378131447097, 0.03708761542948765
-BaggingStree, ionosphere, 0.9510100603621732, 0.02418916928619684
-BaggingStree, iris, 0.9653333333333335, 0.032632635334721105
-BaggingStree, led-display, 0.7218000000000001, 0.027124896313165885
-BaggingStree, libras, 0.8544444444444445, 0.044724810139063854
-BaggingStree, low-res-spect, 0.910904602362899, 0.034998937080933226
-BaggingStree, lymphography, 0.8511724137931036, 0.060541983349731314
-BaggingStree, mammographic, 0.8272695379965458, 0.02450569044585343
-BaggingStree, molec-biol-promoter, 0.8387012987012987, 0.07620183897953593
-BaggingStree, musk-1, 0.9128135964912282, 0.029764363025043043
-BaggingStree, oocytes_merluccius_nucleus_4d, 0.8454954567192731, 0.02495631167694985
-BaggingStree, oocytes_merluccius_states_2f, 0.9220148254423719, 0.01653121938374588
-BaggingStree, oocytes_trisopterus_nucleus_2f, 0.8103038491563082, 0.020971220779186443
-BaggingStree, oocytes_trisopterus_states_5b, 0.9239998799015193, 0.01817568579552671
-BaggingStree, parkinsons, 0.901025641025641, 0.04865312826739729
-BaggingStree, pima, 0.7726398438163143, 0.028486201404109596
-BaggingStree, pittsburg-bridges-MATERIAL, 0.8658874458874459, 0.07136421080457399
-BaggingStree, pittsburg-bridges-REL-L, 0.6818095238095239, 0.09893968478062681
-BaggingStree, pittsburg-bridges-SPAN, 0.7003508771929825, 0.10417934569394204
-BaggingStree, pittsburg-bridges-T-OR-D, 0.8628095238095238, 0.0747571882042698
-BaggingStree, planning, 0.7352702702702704, 0.06697755238925641
-BaggingStree, post-operative, 0.711111111111111, 0.07535922203472521
-BaggingStree, seeds, 0.9576190476190476, 0.028686277108245766
-BaggingStree, statlog-australian-credit, 0.6782608695652174, 0.03904983647915211
-BaggingStree, statlog-german-credit, 0.7656000000000001, 0.028716545753276115
-BaggingStree, statlog-heart, 0.8392592592592593, 0.04262317742463941
-BaggingStree, statlog-image, 0.9613419913419912, 0.007744079362039781
-BaggingStree, statlog-vehicle, 0.8039122868082146, 0.02550795916537555
-BaggingStree, synthetic-control, 0.9868333333333333, 0.009441339358845712
-BaggingStree, tic-tac-toe, 0.9862183682373472, 0.007729402310947018
-BaggingStree, vertebral-column-2clases, 0.8574193548387095, 0.03942352546439275
-BaggingStree, wine, 0.9791746031746031, 0.025059546117196532
-BaggingStree, zoo, 0.9664285714285715, 0.04138270586454204
-SVC, balance-scale, 0.9785600000000001, 0.013711542582802286
-SVC, balloons, 0.86, 0.28501461950807594
-SVC, breast-cancer-wisc-diag, 0.9753951249805931, 0.014597293231205878
-SVC, breast-cancer-wisc-prog, 0.7938589743589745, 0.05657320433780183
-SVC, breast-cancer-wisc, 0.9679527235354572, 0.011995201821390402
-SVC, breast-cancer, 0.7317543859649124, 0.04356606121838112
-SVC, cardiotocography-10clases, 0.8201826014913007, 0.022919732876958584
-SVC, cardiotocography-3clases, 0.9112899199116268, 0.012401790692530392
-SVC, conn-bench-sonar-mines-rocks, 0.8320905923344947, 0.06095204566527709
-SVC, cylinder-bands, 0.7476127926898914, 0.04110007272505122
-SVC, dermatology, 0.972373195112921, 0.015862520651510115
-SVC, echocardiogram, 0.8252991452991455, 0.07337261558656372
-SVC, fertility, 0.88, 0.0547722557505166
-SVC, haberman-survival, 0.7317133791644632, 0.045381650232207534
-SVC, heart-hungarian, 0.8234482758620689, 0.048867109055436626
-SVC, hepatitis, 0.8251612903225807, 0.07040539663356832
-SVC, ilpd-indian-liver, 0.7133952254641912, 0.03827755623741007
-SVC, ionosphere, 0.9532756539235413, 0.02385368651558141
-SVC, iris, 0.966, 0.032991581417756315
-SVC, led-display, 0.7204, 0.02523568901377571
-SVC, libras, 0.7847222222222221, 0.051012132422797105
-SVC, low-res-spect, 0.9016769529183564, 0.03269429070378195
-SVC, lymphography, 0.8471494252873564, 0.05938135729224899
-SVC, mammographic, 0.8267465457685665, 0.022926835290767923
-SVC, molec-biol-promoter, 0.827142857142857, 0.09431966640910128
-SVC, musk-1, 0.9163881578947368, 0.027520787090975468
-SVC, oocytes_merluccius_nucleus_4d, 0.8403060736489717, 0.023610853521046887
-SVC, oocytes_merluccius_states_2f, 0.9197675753228122, 0.016293604623296522
-SVC, oocytes_trisopterus_nucleus_2f, 0.7991268840449167, 0.026483084828854114
-SVC, oocytes_trisopterus_states_5b, 0.9222464420825075, 0.018202901471558224
-SVC, parkinsons, 0.8743589743589744, 0.04565254824298682
-SVC, pima, 0.7695127748068923, 0.02622048017958126
-SVC, pittsburg-bridges-MATERIAL, 0.8629004329004327, 0.06975826134555882
-SVC, pittsburg-bridges-REL-L, 0.6901904761904761, 0.0893877385352908
-SVC, pittsburg-bridges-SPAN, 0.7094736842105265, 0.10829259913995691
-SVC, pittsburg-bridges-T-OR-D, 0.8648095238095237, 0.07364850331687892
-SVC, planning, 0.7352702702702704, 0.06697755238925641
-SVC, post-operative, 0.7022222222222222, 0.0751623756679456
-SVC, seeds, 0.9328571428571428, 0.04059925269913093
-SVC, statlog-australian-credit, 0.6782608695652174, 0.03904983647915211
-SVC, statlog-german-credit, 0.7663999999999999, 0.027166891614610618
-SVC, statlog-heart, 0.8303703703703706, 0.040801301789393805
-SVC, statlog-image, 0.9554545454545453, 0.00732216243366479
-SVC, statlog-vehicle, 0.797883745214062, 0.028612590720497322
-SVC, synthetic-control, 0.9913333333333334, 0.008326663997864544
-SVC, tic-tac-toe, 0.9863236256544502, 0.007669725164798296
-SVC, vertebral-column-2clases, 0.8461290322580644, 0.03927146112240879
-SVC, wine, 0.9807936507936508, 0.02154056565547604
-SVC, zoo, 0.9366666666666668, 0.05165294968232069
-BaggingWodt, balance-scale, 0.8275199999999999, 0.031805810789854096
-BaggingWodt, balloons, 0.63, 0.24333333333333332
-BaggingWodt, breast-cancer-wisc-diag, 0.9767955286446203, 0.012651131794984151
-BaggingWodt, breast-cancer-wisc-prog, 0.8041282051282052, 0.054065511677276364
-BaggingWodt, breast-cancer-wisc, 0.9688067831449128, 0.013501536929852434
-BaggingWodt, breast-cancer, 0.7372958257713248, 0.05019104122389415
-BaggingWodt, cardiotocography-10clases, 0.8420058547362607, 0.02038235146222122
-BaggingWodt, cardiotocography-3clases, 0.9252600938967136, 0.013290240551130103
-BaggingWodt, conn-bench-sonar-mines-rocks, 0.8512891986062716, 0.05425167048127118
-BaggingWodt, cylinder-bands, 0.7726308775937559, 0.043323936964141746
-BaggingWodt, dermatology, 0.9696445760829323, 0.0188524896065306
-BaggingWodt, echocardiogram, 0.7925925925925926, 0.06690912153964222
-BaggingWodt, fertility, 0.875, 0.05852349955359812
-BaggingWodt, haberman-survival, 0.7340137493389741, 0.05579848014720707
-BaggingWodt, heart-hungarian, 0.8210461718293396, 0.05105327558392002
-BaggingWodt, hepatitis, 0.8309677419354838, 0.07456010432616086
-BaggingWodt, ilpd-indian-liver, 0.7071986442676098, 0.03516496095647541
-BaggingWodt, ionosphere, 0.9122696177062375, 0.03896221222978386
-BaggingWodt, iris, 0.9546666666666666, 0.031098410105841596
-BaggingWodt, led-display, 0.7189, 0.027116231301565492
-BaggingWodt, libras, 0.8352777777777779, 0.04347998688200422
-BaggingWodt, low-res-spect, 0.8926309292893672, 0.028538049916854567
-BaggingWodt, lymphography, 0.8715862068965516, 0.05720076110461543
-BaggingWodt, mammographic, 0.8298747841105353, 0.022169546290874684
-BaggingWodt, molec-biol-promoter, 0.8264069264069264, 0.08838486909314487
-BaggingWodt, musk-1, 0.8932872807017544, 0.031704400918302934
-BaggingWodt, oocytes_merluccius_nucleus_4d, 0.8056791009086561, 0.019752083021678597
-BaggingWodt, oocytes_merluccius_states_2f, 0.9211276901004304, 0.01605560855667252
-BaggingWodt, oocytes_trisopterus_nucleus_2f, 0.812938209331652, 0.027686364424624348
-BaggingWodt, oocytes_trisopterus_states_5b, 0.9255353389779618, 0.01698481765795473
-BaggingWodt, parkinsons, 0.9307692307692308, 0.032936493791449035
-BaggingWodt, pima, 0.7510202869026398, 0.03359040798873941
-BaggingWodt, pittsburg-bridges-MATERIAL, 0.8478354978354977, 0.0664135437533315
-BaggingWodt, pittsburg-bridges-REL-L, 0.6874761904761907, 0.08128337095961154
-BaggingWodt, pittsburg-bridges-SPAN, 0.6784795321637428, 0.11377081388352872
-BaggingWodt, pittsburg-bridges-T-OR-D, 0.8570952380952381, 0.07769860077477762
-BaggingWodt, planning, 0.6827927927927929, 0.06607719671456548
-BaggingWodt, post-operative, 0.6455555555555555, 0.07608474807008883
-BaggingWodt, seeds, 0.9371428571428573, 0.04384056871041486
-BaggingWodt, statlog-australian-credit, 0.6365217391304349, 0.03381380252080168
-BaggingWodt, statlog-german-credit, 0.7661, 0.02579321616239434
-BaggingWodt, statlog-heart, 0.8407407407407408, 0.04287347001033417
-BaggingWodt, statlog-image, 0.9749350649350649, 0.0077450472812213905
-BaggingWodt, statlog-vehicle, 0.764779672815872, 0.02887187995824511
-BaggingWodt, synthetic-control, 0.9886666666666667, 0.009539392014169472
-BaggingWodt, tic-tac-toe, 0.9527105148342061, 0.020956068837478
-BaggingWodt, vertebral-column-2clases, 0.8429032258064517, 0.04325588746295833
-BaggingWodt, wine, 0.9831111111111112, 0.020289564182456803
-BaggingWodt, zoo, 0.9574761904761906, 0.038427155222620905
-Cart, balance-scale, 0.7820799999999999, 0.03608203985364464
-Cart, balloons, 0.6833333333333335, 0.26977356760397747
-Cart, breast-cancer-wisc-diag, 0.9239217512808571, 0.02323176886368342
-Cart, breast-cancer-wisc-prog, 0.6914615384615383, 0.07229069176397018
-Cart, breast-cancer-wisc, 0.9433556012332991, 0.020857147642906682
-Cart, breast-cancer, 0.6362613430127042, 0.05493193657305148
-Cart, cardiotocography-10clases, 0.8109128969897819, 0.019011886406374308
-Cart, cardiotocography-3clases, 0.9204613090306545, 0.013845817718125228
-Cart, conn-bench-sonar-mines-rocks, 0.7253890824622532, 0.06929084790257113
-Cart, cylinder-bands, 0.712632781267847, 0.046238358140227635
-Cart, dermatology, 0.9396038504257683, 0.02387579095888003
-Cart, echocardiogram, 0.7443589743589744, 0.08299708625595158
-Cart, fertility, 0.7989999999999999, 0.08395832299420945
-Cart, haberman-survival, 0.6404970914859863, 0.055117499856839475
-Cart, heart-hungarian, 0.750613676212741, 0.04493113662535727
-Cart, hepatitis, 0.7658064516129032, 0.07293747411360528
-Cart, ilpd-indian-liver, 0.6639640436192161, 0.03825985300800366
-Cart, ionosphere, 0.8757907444668007, 0.03797437861573896
-Cart, iris, 0.9386666666666668, 0.04389887368841153
-Cart, led-display, 0.7037, 0.029644729717101474
-Cart, libras, 0.6549999999999998, 0.059210338603413376
-Cart, low-res-spect, 0.8352036677834599, 0.030503013555880365
-Cart, lymphography, 0.7683218390804598, 0.07551790968755526
-Cart, mammographic, 0.7569187176165802, 0.020852411303147095
-Cart, molec-biol-promoter, 0.7158008658008658, 0.08811757165025375
-Cart, musk-1, 0.7768969298245614, 0.043771936691364643
-Cart, oocytes_merluccius_nucleus_4d, 0.7195667144906744, 0.03068883042942922
-Cart, oocytes_merluccius_states_2f, 0.8911927307508368, 0.02477313298773685
-Cart, oocytes_trisopterus_nucleus_2f, 0.7257617246141836, 0.029837690053299604
-Cart, oocytes_trisopterus_states_5b, 0.8702630156728517, 0.025916263242401694
-Cart, parkinsons, 0.8558974358974359, 0.05822037527622443
-Cart, pima, 0.7011722264663441, 0.030849394257539655
-Cart, pittsburg-bridges-MATERIAL, 0.8006493506493506, 0.07641918889128255
-Cart, pittsburg-bridges-REL-L, 0.6171904761904762, 0.10161093599079367
-Cart, pittsburg-bridges-SPAN, 0.5575438596491228, 0.09592147935027655
-Cart, pittsburg-bridges-T-OR-D, 0.8225238095238094, 0.08877499647986477
-Cart, planning, 0.573963963963964, 0.07834935548163284
-Cart, post-operative, 0.5822222222222222, 0.10554970744033704
-Cart, seeds, 0.9161904761904762, 0.04238897730336739
-Cart, statlog-australian-credit, 0.5720289855072465, 0.04059031952408632
-Cart, statlog-german-credit, 0.6897, 0.02526677660486194
-Cart, statlog-heart, 0.7344444444444446, 0.04693777803495507
-Cart, statlog-image, 0.9613419913419914, 0.00934513626156175
-Cart, statlog-vehicle, 0.706384267316394, 0.030533108686824795
-Cart, synthetic-control, 0.9023333333333332, 0.02511307760244982
-Cart, tic-tac-toe, 0.9522851221640489, 0.018529859619663462
-Cart, vertebral-column-2clases, 0.7996774193548387, 0.04231712199258938
-Cart, wine, 0.9015873015873015, 0.0526003102936011
-Cart, zoo, 0.9555714285714286, 0.044427856881917915
-Hedge, balance-scale, 0.9268799999999999, 0.06136308787069482
-Hedge, balloons, 0.6233333333333332, 0.39234126984126966
-Hedge, breast-cancer, 0.7171203871748335, 0.3616679483449606
-Hedge, breast-cancer-wisc, 0.9672425488180886, 0.05157690357564579
-Hedge, breast-cancer-wisc-diag, 0.9346141903431143, 0.07167217360373247
-Hedge, breast-cancer-wisc-prog, 0.726897435897436, 0.30182419575091546
-Hedge, cardiotocography-10clases, 0.8224853907760284, 0.04244383988028859
-Hedge, cardiotocography-3clases, 0.9249283623308479, 0.0602439508982402
-Hedge, conn-bench-sonar-mines-rocks, 0.7111962833914053, 0.29348159104983085
-Hedge, cylinder-bands, 0.7147991623833998, 0.29943308455075124
-Hedge, dermatology, 0.9518918918918922, 0.021132385529569152
-Hedge, echocardiogram, 0.803732193732194, 0.23184391193739048
-Hedge, fertility, 0.8759999999999999, 0.1962085382961812
-Hedge, haberman-survival, 0.7156848228450555, 0.3635029471291943
-Hedge, heart-hungarian, 0.7867971946230274, 0.2657725912072866
-Hedge, hepatitis, 0.7625806451612901, 0.2589931331325924
-Hedge, ilpd-indian-liver, 0.688022399056882, 0.37862669572022073
-Hedge, ionosphere, 0.89456338028169, 0.11778274288375964
-Hedge, iris, 0.9393333333333334, 0.04973986434193759
-Hedge, led-display, 0.7229000000000001, 0.08588914246771266
-Hedge, libras, 0.6302777777777776, 0.05165805396141651
-Hedge, low-res-spect, 0.8227790513137014, 0.041615114646617435
-Hedge, lymphography, 0.748183908045977, 0.13997135664246718
-Hedge, mammographic, 0.8204010146804837, 0.2508836401655701
-Hedge, molec-biol-promoter, 0.7337229437229437, 0.27165754676111714
-Hedge, musk-1, 0.7899144736842106, 0.21840699019515383
-Hedge, oocytes_merluccius_nucleus_4d, 0.733264945002391, 0.30522643614918554
-Hedge, oocytes_merluccius_states_2f, 0.8992137733142032, 0.07844729159018868
-Hedge, oocytes_trisopterus_nucleus_2f, 0.7316975920254607, 0.3022908829038656
-Hedge, oocytes_trisopterus_states_5b, 0.8871872935807361, 0.08801089558713797
-Hedge, parkinsons, 0.8446153846153851, 0.1624820395549004
-Hedge, pima, 0.7442789236906883, 0.3262591809372487
-Hedge, pittsburg-bridges-MATERIAL, 0.8503463203463203, 0.1423410345127055
-Hedge, pittsburg-bridges-REL-L, 0.6393809523809524, 0.2928002682234352
-Hedge, pittsburg-bridges-SPAN, 0.6292982456140352, 0.30123005266829067
-Hedge, pittsburg-bridges-T-OR-D, 0.8336190476190474, 0.2180498486963409
-Hedge, planning, 0.7144144144144144, 0.4080436981633393
-Hedge, post-operative, 0.6911111111111109, 0.2864134132585191
-Hedge, seeds, 0.8942857142857144, 0.07952081943896047
-Hedge, statlog-australian-credit, 0.6427536231884058, 0.41046196215994185
-Hedge, statlog-german-credit, 0.7159, 0.32651908108216554
-Hedge, statlog-heart, 0.7948148148148146, 0.2531165005844386
-Hedge, statlog-image, 0.9604761904761905, 0.012960080161368716
-Hedge, statlog-vehicle, 0.7284852071005916, 0.148665983001905
-Hedge, synthetic-control, 0.8980000000000001, 0.03526071746856307
-Hedge, tic-tac-toe, 0.9832989746945902, 0.0324288588201445
-Hedge, vertebral-column-2clases, 0.8470967741935483, 0.19925244798629155
-Hedge, wine, 0.9791587301587302, 0.016852769344692316
-Hedge, zoo, 0.9298571428571428, 0.022397494870964197
-TBRRoF, balance-scale, 0.901701780173118, 0.022727898539488985
-TBRRoF, balloons, 0.64875, 0.23921130353453926
-TBRRoF, breast-cancer-wisc-diag, 0.9734979808923474, 0.01211458933120479
-TBRRoF, breast-cancer-wisc-prog, 0.8079471788715488, 0.05471208977777354
-TBRRoF, breast-cancer-wisc, 0.9684692187804401, 0.011827258213964388
-TBRRoF, breast-cancer, 0.7354775226702683, 0.04827367740143163
-TBRRoF, cardiotocography-10clases, 0.8288138243181649, 0.01645252887633946
-TBRRoF, cardiotocography-3clases, 0.9224819890962925, 0.011610247810599902
-TBRRoF, conn-bench-sonar-mines-rocks, 0.834423076923077, 0.05150748510916756
-TBRRoF, cylinder-bands, 0.767890625, 0.03324347930143141
-TBRRoF, dermatology, 0.9746077041238331, 0.014736301000625167
-TBRRoF, echocardiogram, 0.8499598214285714, 0.05162023787508073
-TBRRoF, fertility, 0.8810000000000003, 0.060740820180344164
-TBRRoF, haberman-survival, 0.7374089068825913, 0.044260313726551134
-TBRRoF, heart-hungarian, 0.8190036529680362, 0.03888797653544859
-TBRRoF, hepatitis, 0.8227984595635432, 0.0532198072811453
-TBRRoF, ilpd-indian-liver, 0.714239981360671, 0.033670460871576914
-TBRRoF, ionosphere, 0.9459827586206895, 0.02184731993247911
-TBRRoF, iris, 0.9658281358281358, 0.02676690588381821
-TBRRoF, led-display, 0.68436, 0.04995546157031798
-TBRRoF, libras, 0.8801666666666665, 0.03679035193712203
-TBRRoF, low-res-spect, 0.8999957912457909, 0.026355170969855124
-TBRRoF, lymphography, 0.8536486486486488, 0.05135724820856693
-TBRRoF, mammographic, 0.8280522130013831, 0.020736800209974978
-TBRRoF, molec-biol-promoter, 0.8059340659340658, 0.07345265473126247
-TBRRoF, musk-1, 0.8918907563025209, 0.028415223920829437
-TBRRoF, oocytes_merluccius_nucleus_4d, 0.8380727855344474, 0.021149025869688552
-TBRRoF, oocytes_merluccius_states_2f, 0.9260048065918971, 0.01392436830249517
-TBRRoF, oocytes_trisopterus_nucleus_2f, 0.8335307017543863, 0.025458035585743156
-TBRRoF, oocytes_trisopterus_states_5b, 0.931666666666666, 0.01590439823771617
-TBRRoF, parkinsons, 0.9318872549019607, 0.03330032511327817
-TBRRoF, pima, 0.768046875, 0.028059285918720294
-TBRRoF, pittsburg-bridges-MATERIAL, 0.8622252747252745, 0.05272285056647456
-TBRRoF, pittsburg-bridges-REL-L, 0.6864928571428571, 0.08623338832389287
-TBRRoF, pittsburg-bridges-SPAN, 0.6932608695652175, 0.08145095608868286
-TBRRoF, pittsburg-bridges-T-OR-D, 0.8713629629629633, 0.06055900326616563
-TBRRoF, planning, 0.7224751773049648, 0.05837993833973262
-TBRRoF, post-operative, 0.7113068181818182, 0.08162598674627758
-TBRRoF, seeds, 0.9474358974358972, 0.027630596849567368
-TBRRoF, statlog-australian-credit, 0.6782665062817429, 0.029157752633879357
-TBRRoF, statlog-german-credit, 0.7554400000000001, 0.024236821524035296
-TBRRoF, statlog-heart, 0.8349664719878868, 0.0410218444181296
-TBRRoF, statlog-image, 0.9756969824863881, 0.006903978139917843
-TBRRoF, statlog-vehicle, 0.7870681752441981, 0.026493453010463675
-TBRRoF, synthetic-control, 0.9910666666666664, 0.00849185901074688
-TBRRoF, tic-tac-toe, 0.9844217781558706, 0.008102161082608183
-TBRRoF, vertebral-column-2clases, 0.8515140555646883, 0.03497531619650492
-TBRRoF, wine, 0.9804743083003953, 0.020476239593149058
-TBRRoF, zoo, 0.9492615384615388, 0.04884462984878655
-TBRaF, balance-scale, 0.8975269475747183, 0.023461084398242734
-TBRaF, balloons, 0.59875, 0.23763055126492547
-TBRaF, breast-cancer-wisc-diag, 0.9693809711415347, 0.012888038813231393
-TBRaF, breast-cancer-wisc-prog, 0.8002541016406561, 0.05869323051922061
-TBRaF, breast-cancer-wisc, 0.9685330216247806, 0.011892547172713566
-TBRaF, breast-cancer, 0.732153193131391, 0.04414297240873954
-TBRaF, cardiotocography-10clases, 0.8268112132229536, 0.016587713750123762
-TBRaF, cardiotocography-3clases, 0.9193786017390813, 0.011408461529019366
-TBRaF, conn-bench-sonar-mines-rocks, 0.8092307692307693, 0.052101905394016206
-TBRaF, cylinder-bands, 0.748671875, 0.037879462930709165
-TBRaF, dermatology, 0.974489542715349, 0.015244381530713037
-TBRaF, echocardiogram, 0.8456473214285714, 0.06257980864237837
-TBRaF, fertility, 0.8804000000000003, 0.05947711861036103
-TBRaF, haberman-survival, 0.7328744939271254, 0.04650268909435261
-TBRaF, heart-hungarian, 0.819897716894977, 0.040808946053684174
-TBRaF, hepatitis, 0.8162130937098846, 0.057721855658188784
-TBRaF, ilpd-indian-liver, 0.7113035880708294, 0.03241044043427038
-TBRaF, ionosphere, 0.932176245210728, 0.027745840941158267
-TBRaF, iris, 0.9624047124047124, 0.027672604537966147
-TBRaF, led-display, 0.7121600000000001, 0.025910057029585667
-TBRaF, libras, 0.8463888888888887, 0.03918389343605342
-TBRaF, low-res-spect, 0.9000336700336697, 0.023601420494052557
-TBRaF, lymphography, 0.8422972972972973, 0.0589135421754256
-TBRaF, mammographic, 0.827950899031812, 0.021234429745443983
-TBRaF, molec-biol-promoter, 0.7957554945054945, 0.07181386667033816
-TBRaF, musk-1, 0.8739495798319328, 0.0282061820436449
-TBRaF, oocytes_merluccius_nucleus_4d, 0.8366516365300984, 0.02109662119057668
-TBRaF, oocytes_merluccius_states_2f, 0.9237768368047604, 0.015521407216536862
-TBRaF, oocytes_trisopterus_nucleus_2f, 0.8296710526315794, 0.02406624812234792
-TBRaF, oocytes_trisopterus_states_5b, 0.9273903508771925, 0.016976508458830007
-TBRaF, parkinsons, 0.9110845588235295, 0.03900626598564447
-TBRaF, pima, 0.7593229166666666, 0.028362314210191343
-TBRaF, pittsburg-bridges-MATERIAL, 0.8643956043956043, 0.0575076392003003
-TBRaF, pittsburg-bridges-REL-L, 0.6964571428571429, 0.08540119415248532
-TBRaF, pittsburg-bridges-SPAN, 0.6832608695652175, 0.08456145562451363
-TBRaF, pittsburg-bridges-T-OR-D, 0.8654074074074076, 0.059112371460567796
-TBRaF, planning, 0.7097068557919625, 0.055516738956114635
-TBRaF, post-operative, 0.7067613636363635, 0.08307013468275573
-TBRaF, seeds, 0.9433297720797718, 0.03028629022052037
-TBRaF, statlog-australian-credit, 0.6782645014701952, 0.030260627344985316
-TBRaF, statlog-german-credit, 0.74954, 0.026938015580533888
-TBRaF, statlog-heart, 0.8338795154661477, 0.03857659315818731
-TBRaF, statlog-image, 0.9680695515784999, 0.007978763699442637
-TBRaF, statlog-vehicle, 0.7753996172930158, 0.02537062422962424
-TBRaF, synthetic-control, 0.9886666666666664, 0.008239844094951121
-TBRaF, tic-tac-toe, 0.9759070469973432, 0.012840130632245466
-TBRaF, vertebral-column-2clases, 0.8492429722176555, 0.03473914078871642
-TBRaF, wine, 0.9804347826086953, 0.019004456684509077
-TBRaF, zoo, 0.9336076923076927, 0.058309997782000134

results/exreport_accuracy.txt

@@ -1,39 +0,0 @@
----------------------------------------------------------------------
-Friedman test, objetive maximize output variable accuracy. Obtained p-value: 3.5365e-68
-Chi squared with 12 degrees of freedom statistic: 352.8361
-Test rejected: p-value: 3.5365e-68 < 0.0500
----------------------------------------------------------------------
-Control post hoc test for output accuracy
-Adjust method: Holm
-
-Control method: ODTE
-p-values:
-   BaggingStree	0.4919
-         TBRRoF	0.2100
-            SVC	0.0072
-    BaggingWodt	0.0068
-          STree	0.0052
-          TBRaF	0.0022
-   RandomForest	0.0002
-          TBRoF	0.0000
-          Hedge	0.0000
-           Wodt	0.0000
-           Cart	0.0000
-      ExtraTree	0.0000
----------------------------------------------------------------------
-$testMultiple
-               classifier       pvalue      rank win tie loss
-ODTE                 ODTE           NA  3.091837  NA  NA   NA
-BaggingStree BaggingStree 4.918524e-01  3.632653  27   5   17
-TBRRoF             TBRRoF 2.099730e-01  4.367347  34   0   15
-SVC                   SVC 7.221246e-03  5.479592  35   3   11
-BaggingWodt   BaggingWodt 6.791738e-03  5.561224  33   1   15
-STree               STree 5.168361e-03  5.673469  36   6    7
-TBRaF               TBRaF 2.170524e-03  5.897959  39   0   10
-RandomForest RandomForest 2.461390e-04  6.346939  38   0   11
-TBRoF               TBRoF 5.434435e-06  7.000000  43   0    6
-Hedge               Hedge 3.112186e-16  9.724490  47   0    2
-Wodt                 Wodt 2.308557e-19 10.367347  49   0    0
-Cart                 Cart 4.521871e-25 11.408163  48   0    1
-ExtraTree       ExtraTree 1.549559e-31 12.448980  49   0    0
-

src/Results.py

@@ -5,7 +5,6 @@ import json
 import abc
 import shutil
 import subprocess
-from tqdm import tqdm
 import xlsxwriter
 from Experiments import Datasets, BestResults
 from Utils import Folders, Files, Symbols, BEST_ACCURACY_STREE, TextColor
@@ -456,49 +455,56 @@ class SQL(BaseReport):
 
 
 class Benchmark:
-    @staticmethod
-    def get_result_file_name(score):
-        return os.path.join(Folders.results, Files.exreport(score))
+    def __init__(self, score):
+        self._score = score
+        self._results = []
+        self._models = []
+        self._report = {}
+        self._datasets = set()
 
-    @staticmethod
-    def _process_dataset(results, data):
-        model = data["model"]
-        for record in data["results"]:
-            dataset = record["dataset"]
-            if (model, dataset) in results:
-                if record["score"] > results[model, dataset][0]:
-                    results[model, dataset] = (
-                        record["score"],
-                        record["score_std"],
-                    )
-            else:
-                results[model, dataset] = (
-                    record["score"],
-                    record["score_std"],
+    def get_result_file_name(self):
+        return os.path.join(Folders.results, Files.exreport(self._score))
+
+    def compile_results(self):
+        summary = Summary()
+        summary.acquire(given_score=self._score)
+        self._models = summary.get_models()
+        for model in self._models:
+            best = summary.best_result(
+                criterion="model", value=model, score=self._score
+            )
+            file_name = os.path.join(Folders.results, best["file"])
+            with open(file_name) as fi:
+                experiment = json.load(fi)
+                for result in experiment["results"]:
+                    dataset = result["dataset"]
+                    record = {
+                        "model": model,
+                        "dataset": dataset,
+                        "score": result["score"],
+                        "score_std": result["score_std"],
+                        "file_name": file_name,
+                    }
+                    self._results.append(record)
+                    if model not in self._report:
+                        self._report[model] = {}
+                    self._report[model][dataset] = record
+                    self._datasets.add(dataset)
+        self._datasets = sorted(self._datasets)
+
+    def save_results(self):
+        # build Files.exreport
+        result_file_name = self.get_result_file_name()
+        with open(result_file_name, "w") as f:
+            f.write(f"classifier, dataset, {self._score}, stdev, file_name\n")
+            for record in self._results:
+                f.write(
+                    f"{record['model']}, {record['dataset']}, "
+                    f"{record['score']}, {record['score_std']}, "
+                    f"{record['file_name']}\n"
                 )
 
-    @staticmethod
-    def compile_results(score):
-        # build Files.exreport
-        result_file_name = Benchmark.get_result_file_name(score)
-        results = {}
-        init_suffix, end_suffix = Files.results_suffixes(score=score)
-        all_files = list(os.walk(Folders.results))
-        for root, _, files in tqdm(all_files, desc="files"):
-            for name in files:
-                if name.startswith(init_suffix) and name.endswith(end_suffix):
-                    file_name = os.path.join(root, name)
-                    with open(file_name) as fp:
-                        data = json.load(fp)
-                        Benchmark._process_dataset(results, data)
-
-        with open(result_file_name, "w") as f:
-            f.write(f"classifier, dataset, {score}, stdev\n")
-            for (model, dataset), (accuracy, stdev) in results.items():
-                f.write(f"{model}, {dataset}, {accuracy}, {stdev}\n")
-
-    @staticmethod
-    def exreport(score):
+    def exreport(self):
         def end_message(message, file):
             length = 100
             print("*" * length)
@@ -515,74 +521,67 @@ class Benchmark:
             os.remove(Files.exreport_pdf)
         except FileNotFoundError:
             pass
-        except OSError as e:
-            print("Error: %s : %s" % (Folders.report, e.strerror))
+        except OSError as os_error:
+            print("Error: %s : %s" % (Folders.report, os_error.strerror))
         # Compute Friedman & Holm Tests
         fout = open(
-            os.path.join(Folders.results, Files.exreport_output(score)), "w"
+            os.path.join(Folders.results, Files.exreport_output(self._score)),
+            "w",
         )
         ferr = open(
-            os.path.join(Folders.results, Files.exreport_err(score)), "w"
+            os.path.join(Folders.results, Files.exreport_err(self._score)), "w"
         )
         result = subprocess.run(
-            ["Rscript", os.path.join(Folders.src, Files.benchmark_r), score],
+            [
+                "Rscript",
+                os.path.join(Folders.src, Files.benchmark_r),
+                self._score,
+            ],
             stdout=fout,
             stderr=ferr,
         )
         fout.close()
         ferr.close()
         if result.returncode != 0:
-            end_message("Error computing benchmark", Files.exreport_err(score))
+            end_message(
+                "Error computing benchmark", Files.exreport_err(self._score)
+            )
         else:
-            end_message("Benchmark Ok", Files.exreport_output(score))
+            end_message("Benchmark Ok", Files.exreport_output(self._score))
         Files.open(Files.exreport_pdf)
 
-    @staticmethod
-    def build_results(score):
-        # Build results data structure
-        file_name = Benchmark.get_result_file_name(score)
-        results = {}
-        with open(file_name) as f:
-            data = f.read().splitlines()
-            data = data[1:]
-        for line in data:
-            model, dataset, accuracy, stdev = line.split(", ")
-            if model not in results:
-                results[model] = {}
-            results[model][dataset] = (accuracy, stdev)
-        return results
+    def report(self):
+        print(f"{'Dataset':30s} ", end="")
+        lines = "=" * 30 + " "
+        for model in self._models:
+            print(f"{model:^13s} ", end="")
+            lines += "=" * 13 + " "
+        print(f"\n{lines}")
+        for dataset in self._datasets:
+            print(f"{dataset:30s} ", end="")
+            for model in self._models:
+                result = self._report[model][dataset]
+                print(f"{float(result['score']):.5f}±", end="")
+                print(f"{float(result['score_std']):.3f} ", end="")
+            print("")
+        d_name = next(iter(self._datasets))
+        print(f"\n{'Model':30s} {'File Name':75s} Score")
+        print("=" * 30 + " " + "=" * 75 + " ========")
+        for model in self._models:
+            file_name = self._report[model][d_name]["file_name"]
+            report = StubReport(file_name)
+            report.report()
+            print(f"{model:^30s} {file_name:75s} {report.score:8.5f}")
 
-    @staticmethod
-    def report(score):
-        def show(results):
-            datasets = results[list(results)[0]]
-            print(f"{'Dataset':30s} ", end="")
-            lines = "=" * 30 + " "
-            for model in results:
-                print(f"{model:^13s} ", end="")
-                lines += "=" * 13 + " "
-            print(f"\n{lines}")
-            for dataset, _ in datasets.items():
-                print(f"{dataset:30s} ", end="")
-                for model in results:
-                    print(f"{float(results[model][dataset][0]):.5f}±", end="")
-                    print(f"{float(results[model][dataset][1]):.3f} ", end="")
-                print("")
+    def get_excel_file_name(self):
+        return os.path.join(
+            Folders.exreport, Files.exreport_excel(self._score)
+        )
 
-        print(f"* Score is: {score}")
-        show(Benchmark.build_results(score))
-
-    @staticmethod
-    def get_excel_file_name(score):
-        return os.path.join(Folders.exreport, Files.exreport_excel(score))
-
-    @staticmethod
-    def excel(score):
-        results = Benchmark.build_results(score)
-        book = xlsxwriter.Workbook(Benchmark.get_excel_file_name(score))
+    def excel(self):
+        book = xlsxwriter.Workbook(self.get_excel_file_name())
         sheet = book.add_worksheet("Benchmark")
         normal = book.add_format({"font_size": 14})
-        bold = book.add_format({"bold": True, "font_size": 14})
         decimal = book.add_format({"num_format": "0.000000", "font_size": 14})
         merge_format = book.add_format(
             {
@@ -592,22 +591,30 @@ class Benchmark:
                 "font_size": 14,
             }
         )
+        merge_format_normal = book.add_format(
+            {
+                "valign": "vcenter",
+                "font_size": 14,
+            }
+        )
         row = row_init = 4
 
         def header():
             nonlocal row
             sheet.merge_range(0, 0, 1, 0, "Benchmark of Models", merge_format)
-            sheet.write(1, 2, f"Score is {score}", bold)
+            sheet.merge_range(
+                0, 1, 1, 2, f"Score is {self._score}", merge_format
+            )
             sheet.set_row(1, 20)
             # Set columns width
             sheet.set_column(0, 0, 40)
-            for column in range(2 * len(results)):
+            for column in range(2 * len(self._results)):
                 sheet.set_column(column + 1, column + 1, 15)
             # Set report header
             # Merge 2 rows
             sheet.merge_range(row, 0, row + 1, 0, "Dataset", merge_format)
             column = 1
-            for model in results:
+            for model in self._models:
                 # Merge 2 columns
                 sheet.merge_range(
                     row, column, row, column + 1, model, merge_format
@@ -615,42 +622,73 @@ class Benchmark:
                 column += 2
             row += 1
             column = 1
-            for _ in range(len(results)):
+            for _ in range(len(self._results)):
                 sheet.write(row, column, "Score", merge_format)
                 sheet.write(row, column + 1, "Stdev", merge_format)
                 column += 2
 
         def body():
             nonlocal row
-            datasets = results[list(results)[0]]
-            for dataset, _ in datasets.items():
+            for dataset in self._datasets:
                 row += 1
                 sheet.write(row, 0, f"{dataset:30s}", normal)
                 column = 1
-                for model in results:
+                for model in self._models:
                     sheet.write(
                         row,
                         column,
-                        float(results[model][dataset][0]),
+                        float(self._report[model][dataset]["score"]),
                         decimal,
                     )
                     column += 1
                     sheet.write(
                         row,
                         column,
-                        float(results[model][dataset][1]),
+                        float(self._report[model][dataset]["score_std"]),
                         decimal,
                     )
                     column += 1
 
         def footer():
+            nonlocal row
             for c in range(row_init, row + 1):
                 sheet.set_row(c, 20)
 
+        def models_files():
+            nonlocal row
+            row += 2
+            # Set report header
+            # Merge 2 rows
+            sheet.merge_range(row, 0, row + 1, 0, "Model", merge_format)
+            sheet.merge_range(row, 1, row + 1, 5, "File", merge_format)
+            sheet.merge_range(row, 6, row + 1, 6, "Score", merge_format)
+            row += 1
+            d_name = next(iter(self._datasets))
+            for model in self._models:
+                file_name = self._report[model][d_name]["file_name"]
+                report = StubReport(file_name)
+                report.report()
+                row += 1
+                sheet.write(
+                    row,
+                    0,
+                    model,
+                    normal,
+                )
+                sheet.merge_range(
+                    row, 1, row, 5, file_name, merge_format_normal
+                )
+                sheet.write(
+                    row,
+                    6,
+                    report.score,
+                    decimal,
+                )
+
         header()
         body()
         footer()
-
+        models_files()
         book.close()
 
 
@@ -667,6 +705,7 @@ class StubReport(BaseReport):
 
     def footer(self, accuracy: float) -> None:
         self.accuracy = accuracy
+        self.score = accuracy / BEST_ACCURACY_STREE
 
 
 class Summary:
@@ -674,8 +713,12 @@ class Summary:
         self.results = Files().get_all_results()
         self.data = []
         self.datasets = {}
+        self.models = set()
 
-    def acquire(self) -> None:
+    def get_models(self):
+        return self.models
+
+    def acquire(self, given_score="any") -> None:
         """Get all results"""
         for result in self.results:
             (
@@ -686,22 +729,24 @@ class Summary:
                 time,
                 stratified,
             ) = Files().split_file_name(result)
-            report = StubReport(os.path.join(Folders.results, result))
-            report.report()
-            entry = dict(
-                score=score,
-                model=model,
-                title=report.title,
-                platform=platform,
-                date=date,
-                time=time,
-                stratified=stratified,
-                file=result,
-                metric=report.accuracy / BEST_ACCURACY_STREE,
-                duration=report.duration,
-            )
-            self.datasets[result] = report.lines
-            self.data.append(entry)
+            if given_score in ("any", score):
+                self.models.add(model)
+                report = StubReport(os.path.join(Folders.results, result))
+                report.report()
+                entry = dict(
+                    score=score,
+                    model=model,
+                    title=report.title,
+                    platform=platform,
+                    date=date,
+                    time=time,
+                    stratified=stratified,
+                    file=result,
+                    metric=report.score,
+                    duration=report.duration,
+                )
+                self.datasets[result] = report.lines
+                self.data.append(entry)
 
     def list_results(
         self,

src/benchmark.py

@@ -27,10 +27,11 @@ def parse_arguments():
 
 
 (score, excel) = parse_arguments()
-benchmark = Benchmark()
-benchmark.compile_results(score)
-benchmark.report(score)
-benchmark.exreport(score)
+benchmark = Benchmark(score)
+benchmark.compile_results()
+benchmark.save_results()
+benchmark.report()
+benchmark.exreport()
 if excel:
-    benchmark.excel(score)
-    Files.open(benchmark.get_excel_file_name(score))
+    benchmark.excel()
+    Files.open(benchmark.get_excel_file_name())

src/benchmark.r

@@ -7,7 +7,7 @@ csv_file <- glue("results/exreport_{args[1]}.csv")
 destination <- "exreport/"
 results <- read.csv(csv_file)
 library(exreport)
-experiment <- expCreate(results, method="classifier", problem="dataset", name="Stree")
+experiment <- expCreate(results, method="classifier", problem="dataset", name="Ranking")
 testScore <- testMultipleControl(experiment, args[1], "max")
 summary(testScore)
 table1 <- tabularTestSummary(testScore, columns =  c("pvalue", "rank", "wtl"))
@@ -16,7 +16,7 @@ plot1 <- plotExpSummary(experiment, args[1], columns = 3)
 plot2 <- plotCumulativeRank(testScore)
 plot3 <- plotRankDistribution(testScore)
 
-report <- exreport("Stree Report")
+report <- exreport("Ranking Report")
 # Add the experiment object for reference:
 report <- exreportAdd(report, experiment)
 # Now add the test:
@@ -31,7 +31,7 @@ report <- exreportAdd(report, list(plot1, plot2, table1, plot3))
 # We have decided to generate the table at this point of the tutorial to discusse some special formating parameters of this function. Concretely, some of the tabular outputs generated by exreport have some properties that are only useful when rendering the objets in a graphic report, and have no effect in the object representation in the R console. In this case, we will tell the function to boldface the method that maximices the result for each column, and to split the table into to pieces when rendering.
 
 # We create the table:
-table2 <- tabularExpSummary(experiment, args[1], digits=4, format="f", boldfaceColumns="max", tableSplit=2)
+table2 <- tabularExpSummary(experiment, args[1], digits=4, format="f", boldfaceColumns="max", rowsAsMethod=FALSE)
 # And add it to the report:
 report <- exreportAdd(report, table2)
 # Now that we have finished adding elements to the report it is time to render it. We want to generate an HTML report, so we call the appropiate function, by default it renders and opens the report in your browser using a temporary file, but you can optionally specify a folder in which the report will be saved for future use.

src/pair_check.py

@@ -1,8 +1,8 @@
 #!/usr/bin/env python
 import argparse
-from Experiments import Experiment, Datasets
-from Results import Report
-from Utils import EnvDefault
+import os
+from Results import Summary, StubReport
+from Utils import EnvDefault, Folders
 
 """Check best results of two models giving scores and win-tie-loose results
 """
@@ -36,7 +36,8 @@ def parse_arguments():
     args = ap.parse_args()
     return (
         args.score,
-        args.model1 < args.model2,
+        args.model1,
+        args.model2,
     )
 
 
@@ -45,3 +46,29 @@ def parse_arguments():
     model1,
     model2,
 ) = parse_arguments()
+
+summary = Summary()
+summary.acquire()
+win = tie = loose = 0
+best_1 = summary.best_result(criterion="model", value=model1, score=score)
+best_2 = summary.best_result(criterion="model", value=model2, score=score)
+report_1 = StubReport(os.path.join(Folders.results, best_1["file"]))
+report_1.report()
+report_2 = StubReport(os.path.join(Folders.results, best_2["file"]))
+report_2.report()
+for result1, result2 in zip(report_1.lines, report_2.lines):
+    result = result1["score"] - result2["score"]
+    if result > 0:
+        win += 1
+    elif result < 0:
+        loose += 1
+    else:
+        tie += 1
+print(f"{'Model':<20} {'File':<70} {'Score':<10} Win Tie Loose")
+print("=" * 20 + " " + "=" * 70 + " " + "=" * 10 + " === === =====")
+print(f"{model1:<20} {best_1['file']:<70} {report_1.score:10.5f}")
+print(
+    f"{model2:<20} {best_2['file']:<70} "
+    f"{report_2.score:10.5f} "
+    f"{win:3d} {tie:3d} {loose:5d}"
+)