DEVELOPMENT... OpenML
Run
689

Run 689

Task 80 (Learning Curve) car Uploaded 07-04-2014 by Mandy
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Flow

weka.ZeroR(1)Weka implementation of ZeroR
weka.A1DE(2)_F1
weka.A1DE(2)_M1.0
weka.AdaBoostM1_DecisionStump(2)_I10
weka.AdaBoostM1_DecisionStump(2)_P100
weka.AdaBoostM1_DecisionStump(2)_S1
weka.AdaBoostM1_DecisionStump(2)_Wweka.classifiers.trees.DecisionStump
weka.AdaBoostM1_IBk(2)_I20
weka.AdaBoostM1_IBk(2)_P100
weka.AdaBoostM1_IBk(2)_S1
weka.AdaBoostM1_IBk(2)_Wweka.classifiers.lazy.IBk
weka.Bagging_NaiveBayes(2)_I80
weka.Bagging_NaiveBayes(2)_P100
weka.Bagging_NaiveBayes(2)_S1
weka.Bagging_NaiveBayes(2)_Wweka.classifiers.bayes.NaiveBayes
weka.Bagging_NaiveBayes(2)_num-slots1
weka.Bagging_OneR(2)_I160
weka.Bagging_OneR(2)_P100
weka.Bagging_OneR(2)_S1
weka.Bagging_OneR(2)_Wweka.classifiers.rules.OneR
weka.Bagging_OneR(2)_num-slots1
weka.BayesianLogisticRegression(2)_Dtrue
weka.BayesianLogisticRegression(2)_F2
weka.BayesianLogisticRegression(2)_H1
weka.BayesianLogisticRegression(2)_I100
weka.BayesianLogisticRegression(2)_Ntrue
weka.BayesianLogisticRegression(2)_P1
weka.BayesianLogisticRegression(2)_RR:0.01-316,3.16
weka.BayesianLogisticRegression(2)_S0.5
weka.BayesianLogisticRegression(2)_Tl5.0E-4
weka.BayesianLogisticRegression(2)_V0.27
weka.BayesianLogisticRegression(2)_seed1
weka.ComplementNaiveBayes(2)_S1.0
weka.GaussianProcesses_PolyKernel(3)_L1.0
weka.GaussianProcesses_PolyKernel(3)_N0
weka.IBk(2)_Atrue
weka.IBk(2)_K1
weka.IBk(2)_W0
weka.OneR(3)_B6
weka.PolyKernel(4)_C250007
weka.PolyKernel(4)_E1.0
weka.GaussianProcesses_NormalizedPolyKernel(2)_L1.0
weka.GaussianProcesses_NormalizedPolyKernel(2)_N0
weka.GaussianProcesses_NormalizedPolyKernel(2)_Kweka.classifiers.functions.supportVector.NormalizedPolyKernel
weka.NormalizedPolyKernel(2)_E2.0
weka.NormalizedPolyKernel(2)_C250007
weka.CVParameterSelection_ZeroR(2)_X10
weka.CVParameterSelection_ZeroR(2)_S1
weka.CVParameterSelection_ZeroR(2)_Wweka.classifiers.rules.ZeroR
weka.J48(13)_C0.25
weka.J48(13)_M2
weka.J48(28)_C0.25
weka.J48(28)_M2
weka.REPTree(9)_M2
weka.REPTree(9)_V0.001
weka.REPTree(9)_N3
weka.REPTree(9)_S1
weka.REPTree(9)_L-1
weka.REPTree(9)_I0.0
weka.RandomTree(10)_K0
weka.RandomTree(10)_M1.0
weka.RandomTree(10)_V0.001
weka.RandomTree(10)_S1
weka.RandomForest(5)_I100
weka.RandomForest(5)_K0
weka.RandomForest(5)_S1
weka.RandomForest(5)_num-slots1
weka.MultilayerPerceptronCS(1)_Ha
weka.A1DE(4)_F1
weka.A1DE(4)_M1.0
weka.BayesNet_K2(6)_Dtrue
weka.BayesNet_K2(6)_Qweka.classifiers.bayes.net.search.local.K2
weka.K2(5)_P1
weka.K2(5)_SBAYES
weka.LibSVM(2)_S0
weka.LibSVM(2)_K2
weka.LibSVM(2)_D3
weka.LibSVM(2)_G0.0
weka.LibSVM(2)_R0.0
weka.LibSVM(2)_C1.0
weka.LibSVM(2)_N0.5
weka.LibSVM(2)_P0.1
weka.LibSVM(2)_M40.0
weka.LibSVM(2)_E0.001
weka.LibSVM(2)_model/Users/joa/Downloads/weka-3-7-12
weka.LibSVM(2)_seed1
weka.KernelLogisticRegression_RBFKernel(1)_S1
weka.KernelLogisticRegression_RBFKernel(1)_Kweka.classifiers.functions.supportVector.RBFKernel
weka.KernelLogisticRegression_RBFKernel(1)_L0.01
weka.KernelLogisticRegression_RBFKernel(1)_P1
weka.KernelLogisticRegression_RBFKernel(1)_E1
weka.RBFKernel(3)_G0.01
weka.RBFKernel(3)_C250007
weka.Bagging_REPTree(8)_P100
weka.Bagging_REPTree(8)_S1
weka.Bagging_REPTree(8)_num-slots1
weka.Bagging_REPTree(8)_I10
weka.Bagging_REPTree(8)_Wweka.classifiers.trees.REPTree
weka.ADTree(4)_B10
weka.ADTree(4)_E-3
weka.ADTree(4)_S1
weka.LADTree(4)_B10
weka.FilteredClassifier_Discretize_J48(4)_Fweka.filters.supervised.attribute.Discretize
weka.FilteredClassifier_Discretize_J48(4)_Wweka.classifiers.trees.J48
weka.Discretize(3)_Rfirst-last
weka.Discretize(3)_precision6
weka.RandomForest(8)_K0
weka.RandomForest(8)_S1
weka.RandomForest(8)_num-slots1

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

17 Evaluation measures

Area under ROC curve
0.4964
Per class
KB relative information score
0 ± 0.0005
Mean absolute error
0.229 ± 0.0006
Mean prior absolute error
0.229 ± 0.0006
Number of instances
17280
Per class
OS information
[ Sun Microsystems Inc., 1.6.0_30, amd64, Linux, 2.6.32-358.6.2.el6.x86_64 ]
Predictive accuracy
0.7002 ± 0.0022
Prior entropy
1.2058 ± 0.0089
Recall
0.7002 ± 0.0022
Per class
Relative absolute error
1.0002 ± 0.0003
Root mean prior squared error
0.3381 ± 0.001
Root mean squared error
0.3381 ± 0.001
Root relative squared error
1 ± 0
SciMark benchmark
763.2919