132

Number of instances (rows) of the dataset.

8

Number of attributes (columns) of the dataset.

4

Number of distinct values of the target attribute (if it is nominal).

103

Number of missing values in the dataset.

25

Number of instances with at least one value missing.

1

Number of numeric attributes.

0

Number of nominal attributes.

6.7

Mean standard deviation of attributes of the numeric type.

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

43.18

Percentage of instances belonging to the most frequent class.

Minimal entropy among attributes.

11.43

Second quartile (Median) of skewness among attributes of the numeric type.

57

Number of instances belonging to the most frequent class.

131.1

Minimum kurtosis among attributes of the numeric type.

0

Percentage of binary attributes.

6.7

Second quartile (Median) of standard deviation of attributes of the numeric type.

Maximum entropy among attributes.

0.77

Minimum of means among attributes of the numeric type.

18.94

Percentage of instances having missing values.

Third quartile of entropy among attributes.

131.1

Maximum kurtosis among attributes of the numeric type.

Minimal mutual information between the nominal attributes and the target attribute.

9.75

Percentage of missing values.

131.1

Third quartile of kurtosis among attributes of the numeric type.

0.77

Maximum of means among attributes of the numeric type.

The minimal number of distinct values among attributes of the nominal type.

12.5

Percentage of numeric attributes.

0.77

Third quartile of means among attributes of the numeric type.

Maximum mutual information between the nominal attributes and the target attribute.

11.43

Minimum skewness among attributes of the numeric type.

0

Percentage of nominal attributes.

Third quartile of mutual information between the nominal attributes and the target attribute.

The maximum number of distinct values among attributes of the nominal type.

6.7

Minimum standard deviation of attributes of the numeric type.

First quartile of entropy among attributes.

11.43

Third quartile of skewness among attributes of the numeric type.

11.43

Maximum skewness among attributes of the numeric type.

0.76

Percentage of instances belonging to the least frequent class.

131.1

First quartile of kurtosis among attributes of the numeric type.

6.7

Third quartile of standard deviation of attributes of the numeric type.

6.7

Maximum standard deviation of attributes of the numeric type.

Average entropy of the attributes.

1

Number of instances belonging to the least frequent class.

0.77

First quartile of means among attributes of the numeric type.

Standard deviation of the number of distinct values among attributes of the nominal type.

131.1

Mean kurtosis among attributes of the numeric type.

0

Number of binary attributes.

First quartile of mutual information between the nominal attributes and the target attribute.

0.77

Mean of means among attributes of the numeric type.

11.43

First quartile of skewness among attributes of the numeric type.

Average mutual information between the nominal attributes and the target attribute.

6.7

First quartile of standard deviation of attributes of the numeric type.

1

Average class difference between consecutive instances.

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

Second quartile (Median) of entropy among attributes.

Entropy of the target attribute values.

Average number of distinct values among the attributes of the nominal type.

131.1

Second quartile (Median) of kurtosis among attributes of the numeric type.

0.06

Number of attributes divided by the number of instances.

11.43

Mean skewness among attributes of the numeric type.

0.77

Second quartile (Median) of means among attributes of the numeric type.

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.