Data
optdigits

optdigits

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.

65 features

binaryClass (target)nominal2 unique values
0 missing
input1numeric1 unique values
0 missing
input2numeric9 unique values
0 missing
input3numeric17 unique values
0 missing
input4numeric17 unique values
0 missing
input5numeric17 unique values
0 missing
input6numeric17 unique values
0 missing
input7numeric17 unique values
0 missing
input8numeric17 unique values
0 missing
input9numeric4 unique values
0 missing
input10numeric17 unique values
0 missing
input11numeric17 unique values
0 missing
input12numeric17 unique values
0 missing
input13numeric17 unique values
0 missing
input14numeric17 unique values
0 missing
input15numeric17 unique values
0 missing
input16numeric15 unique values
0 missing
input17numeric5 unique values
0 missing
input18numeric17 unique values
0 missing
input19numeric17 unique values
0 missing
input20numeric17 unique values
0 missing
input21numeric17 unique values
0 missing
input22numeric17 unique values
0 missing
input23numeric17 unique values
0 missing
input24numeric9 unique values
0 missing
input25numeric2 unique values
0 missing
input26numeric17 unique values
0 missing
input27numeric17 unique values
0 missing
input28numeric17 unique values
0 missing
input29numeric17 unique values
0 missing
input30numeric17 unique values
0 missing
input31numeric17 unique values
0 missing
input32numeric3 unique values
0 missing
input33numeric2 unique values
0 missing
input34numeric16 unique values
0 missing
input35numeric17 unique values
0 missing
input36numeric17 unique values
0 missing
input37numeric17 unique values
0 missing
input38numeric17 unique values
0 missing
input39numeric15 unique values
0 missing
input40numeric1 unique values
0 missing
input41numeric8 unique values
0 missing
input42numeric17 unique values
0 missing
input43numeric17 unique values
0 missing
input44numeric17 unique values
0 missing
input45numeric17 unique values
0 missing
input46numeric17 unique values
0 missing
input47numeric17 unique values
0 missing
input48numeric7 unique values
0 missing
input49numeric9 unique values
0 missing
input50numeric17 unique values
0 missing
input51numeric17 unique values
0 missing
input52numeric17 unique values
0 missing
input53numeric17 unique values
0 missing
input54numeric17 unique values
0 missing
input55numeric17 unique values
0 missing
input56numeric13 unique values
0 missing
input57numeric2 unique values
0 missing
input58numeric11 unique values
0 missing
input59numeric17 unique values
0 missing
input60numeric17 unique values
0 missing
input61numeric17 unique values
0 missing
input62numeric17 unique values
0 missing
input63numeric17 unique values
0 missing
input64numeric17 unique values
0 missing

107 properties

5620
Number of instances (rows) of the dataset.
65
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.
64
Number of numeric attributes.
1
Number of nominal attributes.
0.82
Average class difference between consecutive instances.
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
53
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
20.3
Third quartile of kurtosis among attributes of the numeric type.
0.94
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
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.02
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
6.52
Maximum standard deviation of attributes of the numeric type.
10.18
Percentage of instances belonging to the least frequent class.
98.46
Percentage of numeric attributes.
9.05
Third quartile of means among attributes of the numeric type.
0.03
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
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
572
Number of instances belonging to the least frequent class.
1.54
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.85
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
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
168.55
Mean kurtosis among attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
4.07
Third quartile of skewness among attributes of the numeric type.
5.87
Third quartile of standard deviation of attributes of the numeric type.
0.94
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
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.02
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
4.91
Mean of means among attributes of the numeric type.
0.05
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.37
First quartile of kurtosis among attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.03
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
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.26
First quartile of means among attributes of the numeric type.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.85
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
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.92
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.
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.94
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.02
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.
-0.33
First quartile of skewness among attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.03
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
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
5.45
Mean skewness among attributes of the numeric type.
0.97
First quartile of standard deviation of attributes of the numeric type.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.85
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
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
89.82
Percentage of instances belonging to the most frequent class.
3.69
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.47
Entropy of the target attribute values.
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
5048
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.08
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.65
Minimum kurtosis among attributes of the numeric type.
4.57
Second quartile (Median) of means among attributes of the numeric type.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.1
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
2807.5
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
11.99
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.56
Second quartile (Median) of skewness among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.01
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.
1.54
Percentage of binary attributes.
4.3
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.04
Error rate 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.
-1.3
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.

7 tasks

559 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
206 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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