Holdout or random subsampling is a technique to evaluate predictive models by partitioning the original sample into a training set to train the model, and a test set to evaluate it. In a k% holdout,…
estimation procedure
Cross-validation is a technique to evaluate predictive models by partitioning the original sample into a training set to train the model, and a test set to evaluate it. In k-fold cross-validation,…
estimation procedure
Cross-validation is a technique to evaluate predictive models by partitioning the original sample into a training set to train the model, and a test set to evaluate it. In k-fold cross-validation,…
estimation procedure
Cross-validation is a technique to evaluate predictive models by partitioning the original sample into a training set to train the model, and a test set to evaluate it. In k-fold cross-validation,…
estimation procedure
Holdout or random subsampling is a technique to evaluate predictive models by partitioning the original sample into a training set to train the model, and a test set to evaluate it. In a k% holdout,…
estimation procedure
Holdout or random subsampling is a technique to evaluate predictive models by partitioning the original sample into a training set to train the model, and a test set to evaluate it. In a k% holdout,…
estimation procedure
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
Number of instances (rows) of the dataset.
data quality
Average class difference between consecutive instances.
data quality
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
Entropy of the target attribute values.
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 divided by the number of instances.
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
Percentage of instances belonging to the most frequent class.
data quality
Number of instances belonging to the most frequent class.
data quality
Maximum entropy among attributes.
data quality
Maximum kurtosis among attributes of the numeric type.
data quality
Maximum of means among attributes of the numeric type.
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
Maximum skewness among attributes of the numeric type.
data quality
Maximum standard deviation of attributes of the numeric type.
data quality
Average entropy of the attributes.
data quality
Mean kurtosis among attributes of the numeric type.
data quality
Mean of means among attributes of the numeric 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
Mean skewness among attributes of the numeric type.
data quality
Mean standard deviation of attributes of the numeric type.
data quality
Minimal entropy among attributes.
data quality
Minimum kurtosis among attributes of the numeric type.
data quality
Minimum of means among attributes of the numeric 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
Minimum skewness among attributes of the numeric type.
data quality
Minimum standard deviation of attributes of the numeric type.
data quality
Percentage of instances belonging to the least frequent class.
data quality
Number of instances belonging to the least frequent class.
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 binary attributes.
data quality
Number of distinct values of the target attribute (if it is nominal).
data quality
Number of attributes (columns) of the dataset.
data quality
Number of instances with at least one value missing.
data quality
Number of missing values in the dataset.
data quality
Number of numeric attributes.
data quality
Number of nominal attributes.
data quality
Percentage of binary attributes.
data quality
Percentage of instances having missing values.
data quality
Percentage of missing values.
data quality
Percentage of numeric attributes.
data quality
Percentage of nominal attributes.
data quality
First quartile of entropy among attributes.
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 entropy among attributes.
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 entropy among attributes.
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