Data
poker

poker

active Sparse_ARFF Publicly available Visibility: public Uploaded 29-08-2014 by aydin demircioglu
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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

19 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.
9.09
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.5
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
90.91
Percentage of numeric attributes.
50.12
Percentage of instances belonging to the most frequent class.
9.09
Percentage of nominal attributes.
513702
Number of instances belonging to the most frequent class.
49.88
Percentage of instances belonging to the least frequent class.
511308
Number of instances belonging to the least frequent class.
1
Number of binary attributes.

15 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
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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