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poker

poker

active ARFF Publicly available Visibility: public Uploaded 15-06-2022 by Frank Wallace
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Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark. Original description: 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!

6 features

Y (target)numeric2 unique values
0 missing
X2numeric13 unique values
0 missing
X4numeric13 unique values
0 missing
X6numeric13 unique values
0 missing
X8numeric13 unique values
0 missing
X10numeric13 unique values
0 missing

19 properties

1022616
Number of instances (rows) of the dataset.
6
Number of attributes (columns) of the dataset.
0
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.
6
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of nominal attributes.
1
Average class difference between consecutive instances.
100
Percentage of numeric attributes.
0
Percentage of missing values.
0
Percentage of instances having missing values.
0
Percentage of binary attributes.
0
Number of binary attributes.
Number of instances belonging to the least frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the most frequent class.
0
Number of attributes divided by the number of instances.

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