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fri_c2_1000_10

fri_c2_1000_10

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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  • binarized_regression_problem mythbusting_1 study_1 study_15 study_20 study_41 study_7
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others as negative ('N').

11 features

binaryClass (target)nominal2 unique values
0 missing
oz1numeric1000 unique values
0 missing
oz2numeric999 unique values
0 missing
oz3numeric1000 unique values
0 missing
oz4numeric999 unique values
0 missing
oz5numeric1000 unique values
0 missing
oz6numeric1000 unique values
0 missing
oz7numeric1000 unique values
0 missing
oz8numeric999 unique values
0 missing
oz9numeric1000 unique values
0 missing
oz10numeric1000 unique values
0 missing

62 properties

1000
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.
1
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.22
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.25
Maximum kurtosis among attributes of the numeric type.
-0
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
-1.02
Third quartile of kurtosis among attributes of the numeric type.
0
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
90.91
Percentage of numeric attributes.
0
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
9.09
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-0.07
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
0.1
Third quartile of skewness among attributes of the numeric type.
0.74
Maximum skewness among attributes of the numeric type.
1
Minimum standard deviation of attributes of the numeric type.
-1.2
First quartile of kurtosis among attributes of the numeric type.
1
Third quartile of standard deviation of attributes of the numeric type.
1
Maximum standard deviation of attributes of the numeric type.
42
Percentage of instances belonging to the least frequent class.
-0
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
420
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
-1
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
-0.05
First quartile of skewness among attributes of the numeric type.
0
Mean of means among attributes of the numeric type.
1
First quartile of standard deviation of attributes of the numeric type.
0.5
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
0.98
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
-1.17
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.01
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
0
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.
0.1
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
58
Percentage of instances belonging to the most frequent class.
1
Mean standard deviation of attributes of the numeric type.
0.05
Second quartile (Median) of skewness among attributes of the numeric type.
580
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

6 tasks

447 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
206 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
0 runs - estimation_procedure: 50 times Clustering
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