Data
poker

poker

active Sparse_ARFF Publicly available Visibility: public Uploaded 29-08-2014 by aydin demircioglu
0 likes downloaded by 17 people , 23 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: UCI Source: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets) - Please cite: This is the poker dataset, retrieved 2013-11-14 from the libSVM site. Additional to the preprocessing done there (see LibSVM site for details), this dataset was created as follows: -join test and train datasets (non-scaled versions) -relabel classes 0=positive class and 1,2,...9=negative class -normalize each file columnwise according to the following rules: -If a column only contains one value (constant feature), it will set to zero and thus removed by sparsity. -If a column contains two values (binary feature), the value occuring more often will be set to zero, the other to one. -If a column contains more than two values (multinary/real feature), the column is divided by its std deviation. NOTE: please keep in mind that poker has a mild redundancy, e.g. some duplicated data points, roughly 0.2%, within each file (train,test). these duplicated points have not been removed!

11 features

Y (target)nominal2 unique values
0 missing
X1numeric4 unique values
0 missing
X2numeric13 unique values
0 missing
X3numeric4 unique values
0 missing
X4numeric13 unique values
0 missing
X5numeric4 unique values
0 missing
X6numeric13 unique values
0 missing
X7numeric4 unique values
0 missing
X8numeric13 unique values
0 missing
X9numeric4 unique values
0 missing
X10numeric13 unique values
0 missing

107 properties

1025010
Number of instances (rows) of the dataset.
11
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.
10
Number of numeric attributes.
1
Number of nominal attributes.
Second quartile (Median) of entropy among attributes.
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0
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
0.42
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
50.12
Percentage of instances belonging to the most frequent class.
1
Mean standard deviation of attributes of the numeric type.
-1.29
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
513702
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
2.05
Second quartile (Median) of means among attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.36
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.5
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
-1.21
Maximum kurtosis among attributes of the numeric type.
1.87
Minimum of means among attributes of the numeric type.
-0
Second quartile (Median) of skewness among attributes of the numeric type.
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
2.24
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
9.09
Percentage of binary attributes.
1
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
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.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.32
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.
-0
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
-1.21
Third quartile of kurtosis among attributes of the numeric type.
0.5
Average class difference between consecutive instances.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0
Maximum skewness among attributes of the numeric type.
1
Minimum standard deviation of attributes of the numeric type.
90.91
Percentage of numeric attributes.
2.24
Third quartile of means among attributes of the numeric type.
0.5
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
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.23
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1
Maximum standard deviation of attributes of the numeric type.
49.88
Percentage of instances belonging to the least frequent class.
9.09
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.5
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
0.32
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
511308
Number of instances belonging to the least frequent class.
First quartile of entropy among attributes.
0
Third quartile of skewness among attributes of the numeric type.
0
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
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-1.29
Mean kurtosis among attributes of the numeric type.
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.36
First quartile of kurtosis among attributes of the numeric type.
1
Third quartile of standard deviation of attributes of the numeric type.
0.5
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
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.23
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
2.05
Mean of means among attributes of the numeric type.
0.49
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.87
First quartile of means among attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.5
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
0.32
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of mutual information between the nominal attributes and the target attribute.
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0
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
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.83
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.
-0
First quartile of skewness among attributes of the numeric type.
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.5
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.
0.23
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.
1
First quartile of standard deviation of attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.5
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
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
-0
Mean skewness among attributes of the numeric type.

7 tasks

23 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Y
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Y
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Y
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Y
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task