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Machine Learning Throwdown

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Services and Algorithms

This section describes each of the services compared in the throwdown and the algorithms/models used.

BigML

Decision trees.

Google Prediction API

Unknown black box model(s).

Prior Knowledge

Veritable API - Nonparametric Bayesian model.

Weka

10 popular algorithms (5 classification and 5 regression) were chosen to evaluate Weka. These were chosen to evaluate a wide variety of algorithms rather than to optimize performance.

Classification

Trees (J48)

  • Classifier: weka.classifiers.trees.J48
  • Parameters: -C 0.25 -M 2

Boosted Trees (Adaboost Classifier with J48 as weak learner)

  • Classifier: weka.classifiers.meta.AdaBoostM1
  • Parameters: -P 100 -S 1 -I 10 -W weka.classifiers.trees.J48 -- -C 0.25 -M 2

Naive Bayes

  • Classifier: weka.classifiers.bayes.NaiveBayes
  • No parameters

SVM (with RBF kernel function)

  • Classifier: weka.classifiers.functions.LibSVM
  • Parameters: -S 0 -K 2 -D 3 -G 0.0 -R 0.0 -N 0.5 -M 40.0 -C 1.0 -E 0.0010 -P 0.1

k-Nearest Neighbor (k = 3)

  • Classifier: weka.classifiers.lazy.IBk
  • Parameters: -K 3 -W 0 -A "weka.core.neighboursearch.LinearNNSearch -A \"weka.core.EuclideanDistance -R first-last\""

Regression

Trees (M5P)

  • Classifier: weka.classifiers.trees.M5P
  • Parameters: -M 4.0

Additive Regression (with M5P as weak classifier)

  • Classifier: weka.classifiers.meta.AdditiveRegression
  • Parameters: -S 1.0 -I 10 -W weka.classifiers.trees.M5P -- -M 4.0

Linear Regression

  • Classifier: weka.classifiers.functions.LinearRegression
  • Parameters: -S 0 -R 1.0E-8

SMOreg (support vector regression)

  • Classifier: weka.classifiers.functions.SMOreg
  • Parameters: -C 1.0 -N 0 -I "weka.classifiers.functions.supportVector.RegSMOImproved -L 0.0010 -W 1 -P 1.0E-12 -T 0.0010 -V" -K "weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0"

k-Nearest Neighbor (k = 3)

  • Classifier: weka.classifiers.lazy.IBk
  • Parameters: -K 3 -W 0 -A "weka.core.neighboursearch.LinearNNSearch -A \"weka.core.EuclideanDistance -R first-last\""

Datasets

The datasets come from the UCI Machine Learning Repository and are relatively clean by machine learning standards. They are split into two categories, classification and regression, based on the type of the field we are trying to predict.

The links in the tables below point to the description of the original datasets on the UCI repository. Some of the datasets were downsampled or modified slightly to get them in a common CSV format for our benchmarking software. The actual data used for the throwdown can be found here.

Classification

In a classification problem, the field we are trying to predict has one of a finite number of possible values. Examples include predicting the type of an iris plant or predicting whether a tumor is malignant or benign.

Dataset # Instances # Attributes Missing Values CSV Size
Breast Cancer Wisconsin (Diagnostic) 699 11 Yes 24K
Glass Identification 214 11 No 17K
Iris 150 5 No 4.5K
Pen-Based Recognition of Handwritten Digits 7494 17 No 381K
Pima Indians Diabetes 768 9 No 28K
Wine 178 14 No 11K

Regression

In a regression problem, the field we are trying to predict has a numeric value. Examples include predicting the fuel efficiency of a car or predicting the number of violent crimes in a community.

Dataset # Instances # Attributes Missing Values CSV Size
Abalone 4177 9 No 187K
Auto MPG 398 8 Yes 14K
Communities and Crime 5822 127 Yes 1.0M
Concrete Compressive Strength 1030 9 No 48K
Insurance Company Benchmark (COIL 2000) 5822 86 No 983K

Results

Dataset: Breast Cancer Wisconsin (Diagnostic) (Classification)

# Algorithm Accuracy
1 Prior Knowledge - Veritable - Classification 0.97
2 Weka k-Nearest Neighbor (k = 3) classifier 0.97
3 Weka Adaboost Classifier with J48 as weak learner 0.97
4 Weka Naive Bayes 0.96
5 Google Predict Classifier 0.96
6 BigML Classification Tree 0.95
7 Weka J48 Tree 0.94
8 Weka SVM (with RBF kernel function) 0.66
# Algorithm Macro Average F1 Score
1 Prior Knowledge - Veritable - Classification 0.96
2 Weka Adaboost Classifier with J48 as weak learner 0.95
3 Weka k-Nearest Neighbor (k = 3) classifier 0.95
4 Weka Naive Bayes 0.94
5 Google Predict Classifier 0.94
6 BigML Classification Tree 0.93
7 Weka J48 Tree 0.91
8 Weka SVM (with RBF kernel function) 0.05
# Algorithm Macro Average Phi Coefficient
1 Prior Knowledge - Veritable - Classification 0.94
2 Weka Adaboost Classifier with J48 as weak learner 0.92
3 Weka k-Nearest Neighbor (k = 3) classifier 0.92
4 Weka Naive Bayes 0.92
5 Google Predict Classifier 0.90
6 BigML Classification Tree 0.89
7 Weka J48 Tree 0.87
8 Weka SVM (with RBF kernel function) 0.10

Dataset: Pima Indians Diabetes (Classification)

# Algorithm Accuracy
1 Google Predict Classifier 0.76
2 Prior Knowledge - Veritable - Classification 0.76
3 Weka Naive Bayes 0.75
4 Weka k-Nearest Neighbor (k = 3) classifier 0.74
5 Weka J48 Tree 0.74
6 Weka Adaboost Classifier with J48 as weak learner 0.73
7 BigML Classification Tree 0.70
8 Weka SVM (with RBF kernel function) 0.65
# Algorithm Macro Average F1 Score
1 Weka Naive Bayes 0.62
2 Google Predict Classifier 0.61
3 Weka Adaboost Classifier with J48 as weak learner 0.59
4 Weka J48 Tree 0.59
5 Weka k-Nearest Neighbor (k = 3) classifier 0.59
6 Prior Knowledge - Veritable - Classification 0.58
7 BigML Classification Tree 0.56
8 Weka SVM (with RBF kernel function) 0.00
# Algorithm Macro Average Phi Coefficient
1 Google Predict Classifier 0.45
2 Weka Naive Bayes 0.44
3 Prior Knowledge - Veritable - Classification 0.44
4 Weka J48 Tree 0.41
5 Weka k-Nearest Neighbor (k = 3) classifier 0.40
6 Weka Adaboost Classifier with J48 as weak learner 0.40
7 BigML Classification Tree 0.34
8 Weka SVM (with RBF kernel function) 0.00

Dataset: Glass Identification (Classification)

# Algorithm Accuracy
1 Weka SVM (with RBF kernel function) 0.98
2 Weka J48 Tree 0.98
3 Weka Adaboost Classifier with J48 as weak learner 0.98
4 BigML Classification Tree 0.97
5 Google Predict Classifier 0.96
6 Prior Knowledge - Veritable - Classification 0.93
7 Weka k-Nearest Neighbor (k = 3) classifier 0.90
8 Weka Naive Bayes 0.83
# Algorithm Macro Average F1 Score
1 Weka SVM (with RBF kernel function) 0.95
2 Weka J48 Tree 0.94
3 Weka Adaboost Classifier with J48 as weak learner 0.94
4 BigML Classification Tree 0.93
5 Google Predict Classifier 0.89
6 Prior Knowledge - Veritable - Classification 0.83
7 Weka Naive Bayes 0.80
8 Weka k-Nearest Neighbor (k = 3) classifier 0.79
# Algorithm Macro Average Phi Coefficient
1 Weka SVM (with RBF kernel function) 0.95
2 Weka J48 Tree 0.94
3 Weka Adaboost Classifier with J48 as weak learner 0.94
4 BigML Classification Tree 0.93
5 Google Predict Classifier 0.89
6 Prior Knowledge - Veritable - Classification 0.83
7 Weka k-Nearest Neighbor (k = 3) classifier 0.78
8 Weka Naive Bayes 0.77

Dataset: Iris (Classification)

# Algorithm Accuracy
1 Google Predict Classifier 0.97
2 Weka SVM (with RBF kernel function) 0.97
3 Prior Knowledge - Veritable - Classification 0.95
4 Weka Naive Bayes 0.95
5 BigML Classification Tree 0.95
6 Weka k-Nearest Neighbor (k = 3) classifier 0.95
7 Weka J48 Tree 0.95
8 Weka Adaboost Classifier with J48 as weak learner 0.93
# Algorithm Macro Average F1 Score
1 Google Predict Classifier 0.97
2 Weka SVM (with RBF kernel function) 0.96
3 BigML Classification Tree 0.95
4 Weka J48 Tree 0.94
5 Weka k-Nearest Neighbor (k = 3) classifier 0.92
6 Prior Knowledge - Veritable - Classification 0.92
7 Weka Naive Bayes 0.92
8 Weka Adaboost Classifier with J48 as weak learner 0.89
# Algorithm Macro Average Phi Coefficient
1 Google Predict Classifier 0.96
2 Weka SVM (with RBF kernel function) 0.95
3 BigML Classification Tree 0.93
4 Weka J48 Tree 0.92
5 Prior Knowledge - Veritable - Classification 0.90
6 Weka Naive Bayes 0.90
7 Weka k-Nearest Neighbor (k = 3) classifier 0.90
8 Weka Adaboost Classifier with J48 as weak learner 0.86

Dataset: Pen-Based Recognition of Handwritten Digits (Classification)

# Algorithm Accuracy
1 Weka k-Nearest Neighbor (k = 3) classifier 0.99
2 Weka Adaboost Classifier with J48 as weak learner 0.99
3 Google Predict Classifier 0.98
4 BigML Classification Tree 0.97
5 Weka J48 Tree 0.96
6 Weka Naive Bayes 0.88
7 Weka SVM (with RBF kernel function) 0.10
# Algorithm Macro Average F1 Score
1 Weka k-Nearest Neighbor (k = 3) classifier 0.99
2 Weka Adaboost Classifier with J48 as weak learner 0.99
3 Google Predict Classifier 0.98
4 BigML Classification Tree 0.96
5 Weka J48 Tree 0.96
6 Weka Naive Bayes 0.88
7 Weka SVM (with RBF kernel function) 0.03
# Algorithm Macro Average Phi Coefficient
1 Weka k-Nearest Neighbor (k = 3) classifier 0.99
2 Weka Adaboost Classifier with J48 as weak learner 0.99
3 Google Predict Classifier 0.98
4 BigML Classification Tree 0.96
5 Weka J48 Tree 0.96
6 Weka Naive Bayes 0.87
7 Weka SVM (with RBF kernel function) 0.04

Dataset: Wine (Classification)

# Algorithm Accuracy
1 Prior Knowledge - Veritable - Classification 0.97
2 Weka Naive Bayes 0.97
3 Google Predict Classifier 0.97
4 Weka Adaboost Classifier with J48 as weak learner 0.97
5 Weka k-Nearest Neighbor (k = 3) classifier 0.96
6 Weka J48 Tree 0.95
7 BigML Classification Tree 0.92
8 Weka SVM (with RBF kernel function) 0.44
# Algorithm Macro Average F1 Score
1 Prior Knowledge - Veritable - Classification 0.98
2 Weka Naive Bayes 0.97
3 Google Predict Classifier 0.97
4 Weka k-Nearest Neighbor (k = 3) classifier 0.96
5 Weka Adaboost Classifier with J48 as weak learner 0.96
6 Weka J48 Tree 0.94
7 BigML Classification Tree 0.90
8 Weka SVM (with RBF kernel function) 0.27
# Algorithm Macro Average Phi Coefficient
1 Prior Knowledge - Veritable - Classification 0.96
2 Weka Naive Bayes 0.95
3 Google Predict Classifier 0.95
4 Weka Adaboost Classifier with J48 as weak learner 0.94
5 Weka k-Nearest Neighbor (k = 3) classifier 0.94
6 Weka J48 Tree 0.92
7 BigML Classification Tree 0.86
8 Weka SVM (with RBF kernel function) 0.13

Dataset: Abalone (Regression)

# Algorithm Mean Squared Error
1 Weka Additive Regression (with M5P as weak classifier) 4.55
2 Weka M5P Tree 4.55
3 Weka Linear Regression 4.91
4 Google Predict Regressor 4.92
5 Weka SMOreg (support vector regression) 5.08
6 Prior Knowledge - Veritable - Regression 5.08
7 Weka k-Nearest Neighbor (k = 3) regressor 5.61
8 BigML Regression Tree 6.97
# Algorithm R-Squared Score
1 Weka Additive Regression (with M5P as weak classifier) 0.56
2 Weka M5P Tree 0.56
3 Weka Linear Regression 0.52
4 Google Predict Regressor 0.52
5 Prior Knowledge - Veritable - Regression 0.51
6 Weka SMOreg (support vector regression) 0.51
7 Weka k-Nearest Neighbor (k = 3) regressor 0.46
8 BigML Regression Tree 0.32

Dataset: Auto MPG (Regression)

# Algorithm Mean Squared Error
1 Weka M5P Tree 7.89
2 Weka Additive Regression (with M5P as weak classifier) 7.90
3 Weka k-Nearest Neighbor (k = 3) regressor 8.88
4 Weka Linear Regression 11.53
5 Weka SMOreg (support vector regression) 12.12
6 BigML Regression Tree 13.45
7 Prior Knowledge - Veritable - Regression 14.92
8 Google Predict Regressor 85.17
# Algorithm R-Squared Score
1 Weka M5P Tree 0.86
2 Weka Additive Regression (with M5P as weak classifier) 0.86
3 Weka k-Nearest Neighbor (k = 3) regressor 0.85
4 Weka Linear Regression 0.80
5 Weka SMOreg (support vector regression) 0.79
6 BigML Regression Tree 0.76
7 Prior Knowledge - Veritable - Regression 0.73
8 Google Predict Regressor -0.52

Dataset: Insurance Company Benchmark (COIL 2000) (Regression)

# Algorithm Mean Squared Error
1 Google Predict Regressor 0.05
2 Weka M5P Tree 0.05
3 Weka Linear Regression 0.05
4 Weka Additive Regression (with M5P as weak classifier) 0.05
5 Weka SMOreg (support vector regression) 0.06
6 Weka k-Nearest Neighbor (k = 3) regressor 0.07
7 BigML Regression Tree 0.10
# Algorithm R-Squared Score
1 Google Predict Regressor 0.04
2 Weka M5P Tree 0.04
3 Weka Linear Regression 0.04
4 Weka Additive Regression (with M5P as weak classifier) 0.02
5 Weka SMOreg (support vector regression) -0.06
6 Weka k-Nearest Neighbor (k = 3) regressor -0.22
7 BigML Regression Tree -0.78

Dataset: Communities and Crime (Regression)

# Algorithm Mean Squared Error
1 Weka M5P Tree 0.02
2 Weka SMOreg (support vector regression) 0.02
3 Weka Linear Regression 0.02
4 Weka Additive Regression (with M5P as weak classifier) 0.02
5 BigML Regression Tree 0.03
6 Weka k-Nearest Neighbor (k = 3) regressor 0.09
7 Google Predict Regressor 0.17
# Algorithm R-Squared Score
1 Weka SMOreg (support vector regression) 0.64
2 Weka M5P Tree 0.64
3 Weka Linear Regression 0.64
4 Weka Additive Regression (with M5P as weak classifier) 0.64
5 BigML Regression Tree 0.36
6 Weka k-Nearest Neighbor (k = 3) regressor -0.65
7 Google Predict Regressor -2.14

Dataset: Concrete Compressive Strength (Regression)

# Algorithm Mean Squared Error
1 Weka Additive Regression (with M5P as weak classifier) 37.46
2 Weka M5P Tree 38.73
3 BigML Regression Tree 48.41
4 Weka k-Nearest Neighbor (k = 3) regressor 81.20
5 Google Predict Regressor 109.04
6 Weka Linear Regression 109.27
7 Prior Knowledge - Veritable - Regression 116.80
8 Weka SMOreg (support vector regression) 119.75
# Algorithm R-Squared Score
1 Weka Additive Regression (with M5P as weak classifier) 0.86
2 Weka M5P Tree 0.86
3 BigML Regression Tree 0.82
4 Weka k-Nearest Neighbor (k = 3) regressor 0.71
5 Google Predict Regressor 0.61
6 Weka Linear Regression 0.60
7 Prior Knowledge - Veritable - Regression 0.58
8 Weka SMOreg (support vector regression) 0.56
results.csv
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cross_validator,cross_validator_seed,dataset,estimator,score_mean,score_metric,score_std,type
10-fold,1,breast_cancer,bigml_classification_tree,0.951226309921962,accuracy,0.016386917052496,classification
10-fold,1,breast_cancer,bigml_classification_tree,0.928196003807927,macro_avg_f1,0.028101360669502,classification
10-fold,1,breast_cancer,bigml_classification_tree,0.891608738468192,macro_avg_phi_coef,0.039332492035009,classification
10-fold,1,breast_cancer,weka_boosted_trees,0.965384615384615,accuracy,0.022793309418442,classification
10-fold,1,breast_cancer,weka_boosted_trees,0.950151005198885,macro_avg_f1,0.034046100841199,classification
10-fold,1,breast_cancer,weka_boosted_trees,0.924307743944268,macro_avg_phi_coef,0.050785360819803,classification
10-fold,1,breast_cancer,weka_j48_tree,0.940914158305462,accuracy,0.0233316048344,classification
10-fold,1,breast_cancer,weka_j48_tree,0.913608253477239,macro_avg_f1,0.036645992774037,classification
10-fold,1,breast_cancer,weka_j48_tree,0.871394669196381,macro_avg_phi_coef,0.052594800049204,classification
10-fold,1,breast_cancer,weka_knn_3_clf,0.965551839464883,accuracy,0.0118029615281,classification
10-fold,1,breast_cancer,weka_knn_3_clf,0.949103825339267,macro_avg_f1,0.020051339461581,classification
10-fold,1,breast_cancer,weka_knn_3_clf,0.923664082753319,macro_avg_phi_coef,0.028459383222066,classification
10-fold,1,breast_cancer,weka_naive_bayes,0.960869565217391,accuracy,0.018389242812246,classification
10-fold,1,breast_cancer,weka_naive_bayes,0.943736439119476,macro_avg_f1,0.028897005829922,classification
10-fold,1,breast_cancer,weka_naive_bayes,0.915430987712097,macro_avg_phi_coef,0.041641500115954,classification
10-fold,1,breast_cancer,weka_svm_rbf,0.664102564102564,accuracy,0.034545435278461,classification
10-fold,1,breast_cancer,weka_svm_rbf,0.049404942883204,macro_avg_f1,0.0407654741452,classification
10-fold,1,breast_cancer,weka_svm_rbf,0.101693553095898,macro_avg_phi_coef,0.083587522106414,classification
10-fold,1,diabetes,bigml_classification_tree,0.704573934837093,accuracy,0.068278635967424,classification
10-fold,1,diabetes,bigml_classification_tree,0.559577181464978,macro_avg_f1,0.10007245150889,classification
10-fold,1,diabetes,bigml_classification_tree,0.341016458258455,macro_avg_phi_coef,0.147369740712292,classification
10-fold,1,diabetes,weka_boosted_trees,0.734210526315789,accuracy,0.025514104512718,classification
10-fold,1,diabetes,weka_boosted_trees,0.59437996031381,macro_avg_f1,0.069297670369576,classification
10-fold,1,diabetes,weka_boosted_trees,0.400992375706477,macro_avg_phi_coef,0.078136961714044,classification
10-fold,1,diabetes,weka_j48_tree,0.735275689223058,accuracy,0.039855977882827,classification
10-fold,1,diabetes,weka_j48_tree,0.594165600696767,macro_avg_f1,0.080564270124875,classification
10-fold,1,diabetes,weka_j48_tree,0.405325558780458,macro_avg_phi_coef,0.096995343829099,classification
10-fold,1,diabetes,weka_knn_3_clf,0.738784461152882,accuracy,0.034612846809588,classification
10-fold,1,diabetes,weka_knn_3_clf,0.588586128735434,macro_avg_f1,0.08722174201729,classification
10-fold,1,diabetes,weka_knn_3_clf,0.403278772386714,macro_avg_phi_coef,0.09220359589005,classification
10-fold,1,diabetes,weka_naive_bayes,0.753571428571428,accuracy,0.048636287267255,classification
10-fold,1,diabetes,weka_naive_bayes,0.623834750948419,macro_avg_f1,0.081635695460394,classification
10-fold,1,diabetes,weka_naive_bayes,0.444925808695859,macro_avg_phi_coef,0.112899862337749,classification
10-fold,1,diabetes,weka_svm_rbf,0.651503759398496,accuracy,0.037567659955953,classification
10-fold,1,diabetes,weka_svm_rbf,0,macro_avg_f1,0,classification
10-fold,1,diabetes,weka_svm_rbf,0,macro_avg_phi_coef,0,classification
10-fold,1,glass,bigml_classification_tree,0.967428571428572,accuracy,0.03021855386567,classification
10-fold,1,glass,bigml_classification_tree,0.934943454116964,macro_avg_f1,0.106892252401758,classification
10-fold,1,glass,bigml_classification_tree,0.931575850170558,macro_avg_phi_coef,0.108091047226758,classification
10-fold,1,glass,weka_boosted_trees,0.976952380952381,accuracy,0.023148147543462,classification
10-fold,1,glass,weka_boosted_trees,0.936527260179434,macro_avg_f1,0.083523592777672,classification
10-fold,1,glass,weka_boosted_trees,0.935405712742695,macro_avg_phi_coef,0.083430615126526,classification
10-fold,1,glass,weka_j48_tree,0.976952380952381,accuracy,0.023148147543462,classification
10-fold,1,glass,weka_j48_tree,0.936527260179434,macro_avg_f1,0.083523592777672,classification
10-fold,1,glass,weka_j48_tree,0.935405712742695,macro_avg_phi_coef,0.083430615126526,classification
10-fold,1,glass,weka_knn_3_clf,0.90152380952381,accuracy,0.075376846141507,classification
10-fold,1,glass,weka_knn_3_clf,0.78677456083648,macro_avg_f1,0.166784058864142,classification
10-fold,1,glass,weka_knn_3_clf,0.778539149581789,macro_avg_phi_coef,0.172134608192175,classification
10-fold,1,glass,weka_naive_bayes,0.830857142857143,accuracy,0.078511318998501,classification
10-fold,1,glass,weka_naive_bayes,0.796917707160102,macro_avg_f1,0.107159628016662,classification
10-fold,1,glass,weka_naive_bayes,0.766899316690241,macro_avg_phi_coef,0.117273292868926,classification
10-fold,1,glass,weka_svm_rbf,0.977714285714286,accuracy,0.028698763869182,classification
10-fold,1,glass,weka_svm_rbf,0.948903232790701,macro_avg_f1,0.079168104014354,classification
10-fold,1,glass,weka_svm_rbf,0.945836818243419,macro_avg_phi_coef,0.08189026708681,classification
10-fold,1,iris,bigml_classification_tree,0.953333333333333,accuracy,0.042687494916219,classification
10-fold,1,iris,bigml_classification_tree,0.949955632602692,macro_avg_f1,0.047659951792652,classification
10-fold,1,iris,bigml_classification_tree,0.929905393298776,macro_avg_phi_coef,0.065947279050746,classification
10-fold,1,iris,weka_boosted_trees,0.926666666666667,accuracy,0.046666666666667,classification
10-fold,1,iris,weka_boosted_trees,0.892455632602692,macro_avg_f1,0.097614092271965,classification
10-fold,1,iris,weka_boosted_trees,0.859587932981316,macro_avg_phi_coef,0.108120729266006,classification
10-fold,1,iris,weka_j48_tree,0.946666666666667,accuracy,0.049888765156986,classification
10-fold,1,iris,weka_j48_tree,0.943288965936025,macro_avg_f1,0.053870960056862,classification
10-fold,1,iris,weka_j48_tree,0.919905393298777,macro_avg_phi_coef,0.075641454807929,classification
10-fold,1,iris,weka_knn_3_clf,0.946666666666667,accuracy,0.049888765156986,classification
10-fold,1,iris,weka_knn_3_clf,0.921175676175676,macro_avg_f1,0.1031606716506,classification
10-fold,1,iris,weka_knn_3_clf,0.898418606543324,macro_avg_phi_coef,0.114343263027383,classification
10-fold,1,iris,weka_naive_bayes,0.953333333333333,accuracy,0.042687494916219,classification
10-fold,1,iris,weka_naive_bayes,0.919814444814445,macro_avg_f1,0.103448583106163,classification
10-fold,1,iris,weka_naive_bayes,0.901060510032628,macro_avg_phi_coef,0.113109152168249,classification
10-fold,1,iris,weka_svm_rbf,0.966666666666667,accuracy,0.044721359549996,classification
10-fold,1,iris,weka_svm_rbf,0.962696007696008,macro_avg_f1,0.050723817236708,classification
10-fold,1,iris,weka_svm_rbf,0.948492918081535,macro_avg_phi_coef,0.069874459271534,classification
10-fold,1,pendigits,bigml_classification_tree,0.965168431746977,accuracy,0.00368184138007,classification
10-fold,1,pendigits,bigml_classification_tree,0.964857053721874,macro_avg_f1,0.003889018461871,classification
10-fold,1,pendigits,bigml_classification_tree,0.96112946093136,macro_avg_phi_coef,0.004260609361135,classification
10-fold,1,pendigits,weka_boosted_trees,0.989725299957269,accuracy,0.002391162807042,classification
10-fold,1,pendigits,weka_boosted_trees,0.989678544944292,macro_avg_f1,0.002708145678401,classification
10-fold,1,pendigits,weka_boosted_trees,0.9885946106445,macro_avg_phi_coef,0.002942036102772,classification
10-fold,1,pendigits,weka_j48_tree,0.96010422041252,accuracy,0.00705779364467,classification
10-fold,1,pendigits,weka_j48_tree,0.960209218586964,macro_avg_f1,0.007099435386359,classification
10-fold,1,pendigits,weka_j48_tree,0.955942975008047,macro_avg_phi_coef,0.007889031922313,classification
10-fold,1,pendigits,weka_knn_3_clf,0.99439677870627,accuracy,0.002213478571591,classification
10-fold,1,pendigits,weka_knn_3_clf,0.994310722301037,macro_avg_f1,0.002253871244657,classification
10-fold,1,pendigits,weka_knn_3_clf,0.993713103609172,macro_avg_phi_coef,0.002490454943369,classification
10-fold,1,pendigits,weka_naive_bayes,0.880571173250922,accuracy,0.009518035393995,classification
10-fold,1,pendigits,weka_naive_bayes,0.877762238744393,macro_avg_f1,0.008662422210273,classification
10-fold,1,pendigits,weka_naive_bayes,0.867212588318019,macro_avg_phi_coef,0.009333127378116,classification
10-fold,1,pendigits,weka_svm_rbf,0.098745206091522,accuracy,0.008869676911991,classification
10-fold,1,pendigits,weka_svm_rbf,0.030874916853079,macro_avg_f1,0.012590420370936,classification
10-fold,1,pendigits,weka_svm_rbf,0.035143388900732,macro_avg_phi_coef,0.021177266591916,classification
10-fold,1,wine,bigml_classification_tree,0.915294117647059,accuracy,0.063963657501497,classification
10-fold,1,wine,bigml_classification_tree,0.898911388425118,macro_avg_f1,0.070986035269931,classification
10-fold,1,wine,bigml_classification_tree,0.864080579687316,macro_avg_phi_coef,0.098251804250762,classification
10-fold,1,wine,weka_boosted_trees,0.966588235294118,accuracy,0.027816502035071,classification
10-fold,1,wine,weka_boosted_trees,0.955805239594713,macro_avg_f1,0.0432054621438,classification
10-fold,1,wine,weka_boosted_trees,0.942457117293388,macro_avg_phi_coef,0.05087614091305,classification
10-fold,1,wine,weka_j48_tree,0.948941176470588,accuracy,0.041294620409207,classification
10-fold,1,wine,weka_j48_tree,0.937564498853973,macro_avg_f1,0.048608671890465,classification
10-fold,1,wine,weka_j48_tree,0.916864597922742,macro_avg_phi_coef,0.062585529460819,classification
10-fold,1,wine,weka_knn_3_clf,0.958588235294118,accuracy,0.04633800894152,classification
10-fold,1,wine,weka_knn_3_clf,0.957058099852218,macro_avg_f1,0.048217487710731,classification
10-fold,1,wine,weka_knn_3_clf,0.939477585662414,macro_avg_phi_coef,0.06667042612509,classification
10-fold,1,wine,weka_naive_bayes,0.968470588235294,accuracy,0.041497237995386,classification
10-fold,1,wine,weka_naive_bayes,0.968590456384574,macro_avg_f1,0.040022051233726,classification
10-fold,1,wine,weka_naive_bayes,0.954068112954002,macro_avg_phi_coef,0.058353386558566,classification
10-fold,1,wine,weka_svm_rbf,0.440235294117647,accuracy,0.120614114870036,classification
10-fold,1,wine,weka_svm_rbf,0.270209880212169,macro_avg_f1,0.092280636765713,classification
10-fold,1,wine,weka_svm_rbf,0.133644503654394,macro_avg_phi_coef,0.121144000422805,classification
10-fold,1,breast_cancer,prior_veritable_clf,0.971014492753623,accuracy,0.018332044406773,classification
10-fold,1,breast_cancer,prior_veritable_clf,0.957713898504596,macro_avg_f1,0.029131025455308,classification
10-fold,1,breast_cancer,prior_veritable_clf,0.936453832370214,macro_avg_phi_coef,0.042031386113793,classification
10-fold,1,diabetes,prior_veritable_clf,0.758270676691729,accuracy,0.042923046544939,classification
10-fold,1,diabetes,prior_veritable_clf,0.581134247690686,macro_avg_f1,0.073936655458044,classification
10-fold,1,diabetes,prior_veritable_clf,0.440124541197298,macro_avg_phi_coef,0.091454780652115,classification
10-fold,1,glass,prior_veritable_clf,0.932380952380952,accuracy,0.072961332286837,classification
10-fold,1,glass,prior_veritable_clf,0.828878277463552,macro_avg_f1,0.198970939100008,classification
10-fold,1,glass,prior_veritable_clf,0.825152346272562,macro_avg_phi_coef,0.202883687133711,classification
10-fold,1,iris,prior_veritable_clf,0.953333333333333,accuracy,0.042687494916219,classification
10-fold,1,iris,prior_veritable_clf,0.920406445406446,macro_avg_f1,0.103560626299866,classification
10-fold,1,iris,prior_veritable_clf,0.901620527055097,macro_avg_phi_coef,0.113127432503581,classification
10-fold,1,wine,prior_veritable_clf,0.974352941176471,accuracy,0.031941988939943,classification
10-fold,1,wine,prior_veritable_clf,0.975441011230485,macro_avg_f1,0.031458321543618,classification
10-fold,1,wine,prior_veritable_clf,0.963103874705891,macro_avg_phi_coef,0.046093331168768,classification
10-fold,1,diabetes,google_predict_clf,0.760401002506266,accuracy,0.048088472399905,classification
10-fold,1,diabetes,google_predict_clf,0.6149472288215,macro_avg_f1,0.069268995528017,classification
10-fold,1,diabetes,google_predict_clf,0.45482121493494,macro_avg_phi_coef,0.109245349668621,classification
10-fold,1,glass,google_predict_clf,0.961904761904762,accuracy,0.041513323271816,classification
10-fold,1,glass,google_predict_clf,0.89,macro_avg_f1,0.147359474805032,classification
10-fold,1,glass,google_predict_clf,0.888869652339296,macro_avg_phi_coef,0.147177689218105,classification
10-fold,1,iris,google_predict_clf,0.973333333333333,accuracy,0.044221663871405,classification
10-fold,1,iris,google_predict_clf,0.969088134088134,macro_avg_f1,0.050917237553235,classification
10-fold,1,iris,google_predict_clf,0.957794361139438,macro_avg_phi_coef,0.069914132640715,classification
10-fold,1,wine,google_predict_clf,0.966588235294118,accuracy,0.038285758552737,classification
10-fold,1,wine,google_predict_clf,0.96756205644313,macro_avg_f1,0.035681382678228,classification
10-fold,1,wine,google_predict_clf,0.952334058224315,macro_avg_phi_coef,0.052106419253482,classification
10-fold,1,pendigits,google_predict_clf,0.983052569428561,accuracy,0.004226069705338,classification
10-fold,1,pendigits,google_predict_clf,0.982762376081588,macro_avg_f1,0.004642463924349,classification
10-fold,1,pendigits,google_predict_clf,0.98095032468926,macro_avg_phi_coef,0.005086024305822,classification
10-fold,1,breast_cancer,google_predict_clf,0.956522,accuracy,0.012551,classification
10-fold,1,breast_cancer,google_predict_clf,0.936725,macro_avg_f1,0.023127,classification
10-fold,1,breast_cancer,google_predict_clf,0.904882,macro_avg_phi_coef,0.031323,classification
10-fold,1,abalone,weka_additive_m5p,4.54992467826173,mean_squared_error,0.69959856521352,regression
10-fold,1,abalone,weka_additive_m5p,0.561441208377698,r2_score,0.043725376290232,regression
10-fold,1,abalone,weka_knn_3_reg,5.60703580539286,mean_squared_error,0.703635281332986,regression
10-fold,1,abalone,weka_knn_3_reg,0.45766452388648,r2_score,0.05292216267921,regression
10-fold,1,abalone,weka_linear_regression,4.91433700570902,mean_squared_error,0.627884170118274,regression
10-fold,1,abalone,weka_linear_regression,0.524356912405225,r2_score,0.049632216308787,regression
10-fold,1,abalone,weka_m5p_tree,4.55110670311694,mean_squared_error,0.699744317807047,regression
10-fold,1,abalone,weka_m5p_tree,0.561339510933719,r2_score,0.043634828015723,regression
10-fold,1,abalone,weka_smo_reg,5.07651287058334,mean_squared_error,0.627170523728217,regression
10-fold,1,abalone,weka_smo_reg,0.50927437907874,r2_score,0.04184211388264,regression
10-fold,1,coil_2000,weka_additive_m5p,0.05488533321859,mean_squared_error,0.005790933239239,regression
10-fold,1,coil_2000,weka_additive_m5p,0.022713030026238,r2_score,0.034373206426247,regression
10-fold,1,coil_2000,weka_knn_3_reg,0.068336849984112,mean_squared_error,0.006212479887392,regression
10-fold,1,coil_2000,weka_knn_3_reg,-0.219439432668953,r2_score,0.062711362336138,regression
10-fold,1,coil_2000,weka_linear_regression,0.054065457252389,mean_squared_error,0.005004972623676,regression
10-fold,1,coil_2000,weka_linear_regression,0.035925346751917,r2_score,0.037764920820893,regression
10-fold,1,coil_2000,weka_m5p_tree,0.053966083939415,mean_squared_error,0.005419128007854,regression
10-fold,1,coil_2000,weka_m5p_tree,0.038481984868146,r2_score,0.03670255821291,regression
10-fold,1,coil_2000,weka_smo_reg,0.059667425227722,mean_squared_error,0.006258612638789,regression
10-fold,1,coil_2000,weka_smo_reg,-0.06175230019412,r2_score,0.007244003730246,regression
10-fold,1,abalone,bigml_regression_tree,6.97421088643514,mean_squared_error,0.579670182091557,regression
10-fold,1,abalone,bigml_regression_tree,0.322677436860679,r2_score,0.06526995008768,regression
10-fold,1,coil_2000,bigml_regression_tree,0.099562076226359,mean_squared_error,0.008063907499774,regression
10-fold,1,coil_2000,bigml_regression_tree,-0.784941265905169,r2_score,0.187188130886462,regression
10-fold,1,concrete,bigml_regression_tree,48.4123000459789,mean_squared_error,19.516299059764,regression
10-fold,1,concrete,bigml_regression_tree,0.823146416957591,r2_score,0.071772768042928,regression
10-fold,1,concrete,weka_additive_m5p,37.4607368087379,mean_squared_error,6.70626396022337,regression
10-fold,1,concrete,weka_additive_m5p,0.862849721550737,r2_score,0.027888157379538,regression
10-fold,1,concrete,weka_knn_3_reg,81.1957176951456,mean_squared_error,17.8455929410339,regression
10-fold,1,concrete,weka_knn_3_reg,0.70602437564346,r2_score,0.058452678003436,regression
10-fold,1,concrete,weka_linear_regression,109.274176883495,mean_squared_error,15.2013648348219,regression
10-fold,1,concrete,weka_linear_regression,0.60452015593516,r2_score,0.039183883223734,regression
10-fold,1,concrete,weka_m5p_tree,38.7255184359223,mean_squared_error,5.13677494328826,regression
10-fold,1,concrete,weka_m5p_tree,0.858964009431649,r2_score,0.018994556251257,regression
10-fold,1,concrete,weka_smo_reg,119.754272932039,mean_squared_error,22.439945387393,regression
10-fold,1,concrete,weka_smo_reg,0.56419613372891,r2_score,0.081814819129096,regression
10-fold,1,communities,weka_additive_m5p,0.019464509409114,mean_squared_error,0.002293895461941,regression
10-fold,1,communities,weka_additive_m5p,0.635899303641782,r2_score,0.050071880383268,regression
10-fold,1,communities,weka_knn_3_reg,0.087348456835904,mean_squared_error,0.010139704385443,regression
10-fold,1,communities,weka_knn_3_reg,-0.651912667472884,r2_score,0.332459741843092,regression
10-fold,1,communities,weka_linear_regression,0.019405039394014,mean_squared_error,0.002481980609832,regression
10-fold,1,communities,weka_linear_regression,0.636433465091892,r2_score,0.057352807562634,regression
10-fold,1,communities,weka_m5p_tree,0.019202958667723,mean_squared_error,0.001792032582392,regression
10-fold,1,communities,weka_m5p_tree,0.640025896469511,r2_score,0.049425870692006,regression
10-fold,1,communities,weka_smo_reg,0.01922243913657,mean_squared_error,0.003092471920479,regression
10-fold,1,communities,weka_smo_reg,0.641103992684875,r2_score,0.056554694297154,regression
10-fold,1,communities,bigml_regression_tree,0.033733325583782,mean_squared_error,0.004224716239447,regression
10-fold,1,communities,bigml_regression_tree,0.36247891024832,r2_score,0.139668357939383,regression
10-fold,1,auto_mpg,weka_additive_m5p,7.90250490654664,mean_squared_error,2.72153795691769,regression
10-fold,1,auto_mpg,weka_additive_m5p,0.86428278948242,r2_score,0.034031319668874,regression
10-fold,1,auto_mpg,weka_knn_3_reg,8.88153111451173,mean_squared_error,4.12348660223311,regression
10-fold,1,auto_mpg,weka_knn_3_reg,0.848152834944264,r2_score,0.059070982707936,regression
10-fold,1,auto_mpg,weka_linear_regression,11.532176500982,mean_squared_error,3.56939821173046,regression
10-fold,1,auto_mpg,weka_linear_regression,0.802126652606469,r2_score,0.03247599931947,regression
10-fold,1,auto_mpg,weka_m5p_tree,7.89340639536279,mean_squared_error,2.72334051708283,regression
10-fold,1,auto_mpg,weka_m5p_tree,0.864490667055228,r2_score,0.033868028722113,regression
10-fold,1,auto_mpg,weka_smo_reg,12.1218403062193,mean_squared_error,4.72454728862622,regression
10-fold,1,auto_mpg,weka_smo_reg,0.794097443903468,r2_score,0.048443160094442,regression
10-fold,1,auto_mpg,bigml_regression_tree,13.4463127683323,mean_squared_error,4.69563179693518,regression
10-fold,1,auto_mpg,bigml_regression_tree,0.76487398046169,r2_score,0.076827702909823,regression
10-fold,1,auto_mpg,prior_veritable_reg,14.9248392432455,mean_squared_error,4.47928145806367,regression
10-fold,1,auto_mpg,prior_veritable_reg,0.734196141178405,r2_score,0.087080143963041,regression
10-fold,1,concrete,prior_veritable_reg,116.801297102311,mean_squared_error,14.4501273100566,regression
10-fold,1,concrete,prior_veritable_reg,0.576098821804962,r2_score,0.040996457696531,regression
10-fold,1,abalone,prior_veritable_reg,5.08258053934211,mean_squared_error,0.701936050215711,regression
10-fold,1,abalone,prior_veritable_reg,0.509517745364222,r2_score,0.043877113232832,regression
10-fold,1,concrete,google_predict_reg,109.037305585005,mean_squared_error,15.2893425822412,regression
10-fold,1,concrete,google_predict_reg,0.605384256805329,r2_score,0.039555602560217,regression
10-fold,1,abalone,google_predict_reg,4.9150511508671,mean_squared_error,0.62649331426591,regression
10-fold,1,abalone,google_predict_reg,0.524291566090047,r2_score,0.049415635171014,regression
10-fold,1,coil_2000,google_predict_reg,0.053883740736006,mean_squared_error,0.005111250515672,regression
10-fold,1,coil_2000,google_predict_reg,0.039387872600548,r2_score,0.036871967301275,regression
10-fold,1,auto_mpg,google_predict_reg,85.1716775639449,mean_squared_error,107.159988934036,regression
10-fold,1,auto_mpg,google_predict_reg,-0.518749288218349,r2_score,1.70531183149744,regression
10-fold,1,communities,google_predict_reg,0.166552670127419,mean_squared_error,0.036545666627393,regression
10-fold,1,communities,google_predict_reg,-2.14488294659385,r2_score,0.872124135741322,regression

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