Data
pendigits

pendigits

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun.

17 features

binaryClass (target)nominal2 unique values
0 missing
input1numeric101 unique values
0 missing
input2numeric96 unique values
0 missing
input3numeric101 unique values
0 missing
input4numeric98 unique values
0 missing
input5numeric101 unique values
0 missing
input6numeric101 unique values
0 missing
input7numeric101 unique values
0 missing
input8numeric101 unique values
0 missing
input9numeric101 unique values
0 missing
input10numeric101 unique values
0 missing
input11numeric101 unique values
0 missing
input12numeric101 unique values
0 missing
input13numeric101 unique values
0 missing
input14numeric101 unique values
0 missing
input15numeric101 unique values
0 missing
input16numeric101 unique values
0 missing

107 properties

10992
Number of instances (rows) of the dataset.
17
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
16
Number of numeric attributes.
1
Number of nominal attributes.
Maximum entropy among attributes.
-1.69
Minimum kurtosis among attributes of the numeric type.
48.53
Second quartile (Median) of means among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
3.05
Maximum kurtosis among attributes of the numeric type.
28.85
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.02
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.1
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
85.12
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.09
Second quartile (Median) of skewness among attributes of the numeric type.
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
5.88
Percentage of binary attributes.
30.24
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
-1.49
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.93
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.95
Maximum skewness among attributes of the numeric type.
16.22
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
-0.26
Third quartile of kurtosis among attributes of the numeric type.
0.81
Average class difference between consecutive instances.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
41.76
Maximum standard deviation of attributes of the numeric type.
10.41
Percentage of instances belonging to the least frequent class.
94.12
Percentage of numeric attributes.
59.6
Third quartile of means among attributes of the numeric type.
0.98
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
0.01
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
1144
Number of instances belonging to the least frequent class.
5.88
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.02
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
0.93
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.5
Mean kurtosis among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.45
Third quartile of skewness among attributes of the numeric type.
0.88
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
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
50.71
Mean of means among attributes of the numeric type.
0.03
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.2
First quartile of kurtosis among attributes of the numeric type.
34.23
Third quartile of standard deviation of attributes of the numeric type.
0.98
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
0.01
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
35.91
First quartile of means among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.02
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
0.93
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.02
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.88
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
-0.43
First quartile of skewness among attributes of the numeric type.
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.98
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
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
-0.04
Mean skewness among attributes of the numeric type.
26.51
First quartile of standard deviation of attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.02
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
0
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
89.59
Percentage of instances belonging to the most frequent class.
29.77
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.02
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.88
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
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
9848
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.78
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.48
Entropy of the target attribute values.

7 tasks

459 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
217 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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