Measure

The macro unweighted (ignoring class size) average Recall. In macro-averaging, Recall is computed locally over each category ?rst and then the average over all categories is taken, weighted by the…

evaluation measure

The number of milliseconds from the start of training until the completion of testing. Thus, involves both training and testing. Does not take into account the number of cores.

evaluation measure

The number of milliseconds from the start of training until the completion of training. Does not take into account the number of cores.

evaluation measure

The number of milliseconds from the start of testing until the completion of testing. Does not take into account the number of cores.

evaluation measure

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

data quality

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

data quality

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

data quality

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

data quality

Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

data quality

Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

data quality

Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

data quality

Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

data quality

Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

data quality

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump

data quality

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump

data quality

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

data quality

The predictive accuracy obtained by always predicting the majority class.

data quality

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

data quality

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

data quality

Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001

data quality

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001

data quality

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

data quality

Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001

data quality

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

data quality

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001

data quality

Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001

data quality

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001

data quality

Maximum mutual information between the nominal attributes and the target attribute.

data quality

The maximum number of distinct values among attributes of the nominal type.

data quality

Average mutual information between the nominal attributes and the target attribute.

data quality

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

data quality

Average number of distinct values among the attributes of the nominal type.

data quality

Minimal mutual information between the nominal attributes and the target attribute.

data quality

The minimal number of distinct values among attributes of the nominal type.

data quality

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes

data quality

Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes

data quality

Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes

data quality

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

data quality

Number of instances with at least one value missing.

data quality

First quartile of kurtosis among attributes of the numeric type.

data quality

First quartile of means among attributes of the numeric type.

data quality

First quartile of mutual information between the nominal attributes and the target attribute.

data quality

First quartile of skewness among attributes of the numeric type.

data quality

First quartile of standard deviation of attributes of the numeric type.

data quality

Second quartile (Median) of kurtosis among attributes of the numeric type.

data quality

Second quartile (Median) of means among attributes of the numeric type.

data quality

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

data quality

Second quartile (Median) of skewness among attributes of the numeric type.

data quality

Second quartile (Median) of standard deviation of attributes of the numeric type.

data quality

Third quartile of kurtosis among attributes of the numeric type.

data quality

Third quartile of means among attributes of the numeric type.

data quality

Third quartile of mutual information between the nominal attributes and the target attribute.

data quality

Third quartile of skewness among attributes of the numeric type.

data quality

Third quartile of standard deviation of attributes of the numeric type.

data quality

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

data quality

Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1

data quality

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1

data quality

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

data quality

Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2

data quality

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2

data quality

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3

data quality

Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3

data quality

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3

data quality