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Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2460
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uploader_id : 62 - estimation_procedure : Custom 10-fold Crossvalidation - target_feature : pXC50
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3219
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uploader_id : 62 - estimation_procedure : Custom 10-fold Crossvalidation - target_feature : pXC50
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL261
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uploader_id : 62 - estimation_procedure : Custom 10-fold Crossvalidation - target_feature : pXC50
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5154
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uploader_id : 62 - estimation_procedure : Custom 10-fold Crossvalidation - target_feature : pXC50
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4955
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uploader_id : 62 - estimation_procedure : Custom 10-fold Crossvalidation - target_feature : pXC50
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5348
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uploader_id : 62 - estimation_procedure : Custom 10-fold Crossvalidation - target_feature : pXC50
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3277
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uploader_id : 62 - estimation_procedure : Custom 10-fold Crossvalidation - target_feature : pXC50
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5817
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uploader_id : 62 - estimation_procedure : Custom 10-fold Crossvalidation - target_feature : pXC50
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5114
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uploader_id : 62 - estimation_procedure : Custom 10-fold Crossvalidation - target_feature : pXC50
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4424
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uploader_id : 62 - estimation_procedure : Custom 10-fold Crossvalidation - target_feature : pXC50
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2446
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uploader_id : 62 - estimation_procedure : Custom 10-fold Crossvalidation - target_feature : pXC50
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2111338
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uploader_id : 62 - estimation_procedure : Custom 10-fold Crossvalidation - target_feature : pXC50
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4713
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uploader_id : 62 - estimation_procedure : Custom 10-fold Crossvalidation - target_feature : pXC50
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2424
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uploader_id : 62 - estimation_procedure : Custom 10-fold Crossvalidation - target_feature : pXC50
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4100
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uploader_id : 62 - estimation_procedure : Custom 10-fold Crossvalidation - target_feature : pXC50
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4697
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uploader_id : 62 - estimation_procedure : Custom 10-fold Crossvalidation - target_feature : pXC50
Learning Curve on bridges
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uploader_id : 1 - estimation_procedure : 10 times 10-fold Learning Curve - evaluation_measures : predictive_accuracy - number_samples : 3 - target_feature : TYPE
Learning Curve on bridges
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uploader_id : 1 - estimation_procedure : 10 times 10-fold Learning Curve - evaluation_measures : predictive_accuracy - number_samples : 3 - target_feature : TYPE
Learning Curve on colic
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uploader_id : 1 - estimation_procedure : 10 times 10-fold Learning Curve - evaluation_measures : predictive_accuracy - number_samples : 6 - target_feature : pathology_cp_data
Learning Curve on cylinder-bands
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uploader_id : 1 - estimation_procedure : 10 times 10-fold Learning Curve - evaluation_measures : predictive_accuracy - number_samples : 7 - target_feature : band_type
Learning Curve on bridges
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uploader_id : 1 - estimation_procedure : 10-fold Learning Curve - evaluation_measures : predictive_accuracy - number_samples : 3 - target_feature : TYPE
Learning Curve on bridges
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uploader_id : 1 - estimation_procedure : 10-fold Learning Curve - evaluation_measures : predictive_accuracy - number_samples : 3 - target_feature : TYPE
Learning Curve on colic
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uploader_id : 1 - estimation_procedure : 10-fold Learning Curve - evaluation_measures : predictive_accuracy - number_samples : 6 - target_feature : pathology_cp_data
Learning Curve on cylinder-bands
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uploader_id : 1 - estimation_procedure : 10-fold Learning Curve - evaluation_measures : predictive_accuracy - number_samples : 7 - target_feature : band_type
Supervised Classification on bridges
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : TYPE
Supervised Classification on colic
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : pathology_cp_data
Supervised Classification on cylinder-bands
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : band_type
Supervised Classification on bridges
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : TYPE
Supervised Classification on bridges
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : TYPE
Learning Curve on splice
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uploader_id : 1 - estimation_procedure : 10 times 10-fold Learning Curve - evaluation_measures : predictive_accuracy - number_samples : 12 - target_feature : Class
Supervised Classification on splice
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : Class
Supervised Classification on BNG(anneal,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(anneal.ORIG,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(kr-vs-kp)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(letter,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(autos,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : symboling
Supervised Classification on BNG(lymph,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(breast-cancer,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : Class
Supervised Classification on BNG(mfeat-fourier,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(mfeat-karhunen,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(bridges_version1,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : TYPE
Supervised Classification on BNG(bridges_version2,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : TYPE
Supervised Classification on BNG(mfeat-zernike,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(mushroom)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(colic.ORIG,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : pathology_cp_data
Supervised Classification on BNG(colic,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : surgical_lesion
Supervised Classification on BNG(optdigits)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(credit-g,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(pendigits,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(cylinder-bands,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : band_type
Supervised Classification on BNG(dermatology,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(segment)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(sick,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : Class
Supervised Classification on colic
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : pathology_cp_data
Supervised Classification on cylinder-bands
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : band_type
Supervised Classification on BNG(anneal,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(anneal.ORIG,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(kr-vs-kp)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(labor,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(letter,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(lymph,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(breast-cancer,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : Class
Supervised Classification on BNG(mfeat-fourier,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(mfeat-karhunen,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(bridges_version1,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : TYPE
Supervised Classification on BNG(bridges_version2,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : TYPE
Supervised Classification on BNG(mfeat-zernike,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(colic.ORIG,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : pathology_cp_data
Supervised Classification on BNG(colic,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : surgical_lesion
Supervised Classification on BNG(optdigits)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(credit-a,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(credit-g,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(pendigits,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(cylinder-bands,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : band_type
Supervised Classification on BNG(dermatology,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(segment)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(sick,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : Class
Supervised Classification on BNG(sonar,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : Class
Supervised Classification on BNG(soybean)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(spambase)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(heart-c,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : num
Supervised Classification on BNG(heart-h,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : num
Supervised Classification on BNG(vehicle,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : Class
Supervised Classification on BNG(hepatitis,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : Class
Supervised Classification on BNG(hypothyroid,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : Class
Supervised Classification on BNG(vowel,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : Class
Supervised Classification on BNG(ionosphere)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(waveform-5000,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on BNG(zoo,nominal,1000000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : type
Supervised Classification on CovPokElec
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on Hyperplane_10_1E-3
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on Hyperplane_10_1E-4
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on RandomRBF_0_0
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on RandomRBF_10_1E-3
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on RandomRBF_10_1E-4
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on RandomRBF_50_1E-3
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on RandomRBF_50_1E-4
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on SEA(50)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on SEA(50000)
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uploader_id : 1 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class
Learning Curve on molecular-biology_promoters
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uploader_id : 1 - estimation_procedure : 10-fold Learning Curve - evaluation_measures : predictive_accuracy - number_samples : 3 - target_feature : class