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ML2017-challenge-1

ML2017-challenge-1

in_preparation ARFF Publicly available Visibility: public Uploaded 09-06-2017 by Jan van Rijn
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Machine Learning Summer Course Data Mining Challenge

37 features

A37 (target)nominal2 unique values
0 missing
A1nominal2 unique values
0 missing
A2nominal2 unique values
0 missing
A3nominal2 unique values
0 missing
A4nominal2 unique values
0 missing
A5nominal2 unique values
0 missing
A6nominal2 unique values
0 missing
A7nominal2 unique values
0 missing
A8nominal2 unique values
0 missing
A9nominal2 unique values
0 missing
A10nominal2 unique values
0 missing
A11nominal2 unique values
0 missing
A12nominal2 unique values
0 missing
A13nominal2 unique values
0 missing
A14nominal2 unique values
0 missing
A15nominal3 unique values
0 missing
A16nominal2 unique values
0 missing
A17nominal2 unique values
0 missing
A18nominal2 unique values
0 missing
A19nominal2 unique values
0 missing
A20nominal2 unique values
0 missing
A21nominal2 unique values
0 missing
A22nominal2 unique values
0 missing
A23nominal2 unique values
0 missing
A24nominal2 unique values
0 missing
A25nominal2 unique values
0 missing
A26nominal2 unique values
0 missing
A27nominal2 unique values
0 missing
A28nominal2 unique values
0 missing
A29nominal2 unique values
0 missing
A30nominal2 unique values
0 missing
A31nominal2 unique values
0 missing
A32nominal2 unique values
0 missing
A33nominal2 unique values
0 missing
A34nominal2 unique values
0 missing
A35nominal2 unique values
0 missing
A36nominal2 unique values
0 missing

62 properties

3196
Number of instances (rows) of the dataset.
37
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.
0
Number of numeric attributes.
37
Number of nominal attributes.
First quartile of skewness among attributes of the numeric type.
Mean of means among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
1
Average class difference between consecutive instances.
0.02
Average mutual information between the nominal attributes and the target attribute.
0.68
Second quartile (Median) of entropy among attributes.
1
Entropy of the target attribute values.
29.81
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.01
Number of attributes divided by the number of instances.
2.03
Average number of distinct values among the attributes of the nominal type.
Second quartile (Median) of means among attributes of the numeric type.
52.14
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Mean skewness among attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
52.22
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of skewness among attributes of the numeric type.
1669
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
94.59
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
1
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
0.91
Third quartile of entropy among attributes.
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.2
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
100
Percentage of nominal attributes.
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
3
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
0.29
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
47.78
Percentage of instances belonging to the least frequent class.
1527
Number of instances belonging to the least frequent class.
First quartile of means among attributes of the numeric type.
0.16
Standard deviation of the number of distinct values among attributes of the nominal type.
0.59
Average entropy of the attributes.
35
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
Mean kurtosis among attributes of the numeric type.

11 tasks

188 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: A37
59 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: A37
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: A1
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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