Data
MC2

MC2

in_preparation ARFF Publicly available Visibility: public Uploaded 23-06-2017 by Silvia Nunes
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software fault prediction

40 features

Class (target)nominal2 unique values
0 missing
V1numeric35 unique values
0 missing
V2numeric32 unique values
0 missing
V3numeric13 unique values
0 missing
V4numeric16 unique values
0 missing
V5numeric41 unique values
0 missing
V6numeric38 unique values
0 missing
V7numeric28 unique values
0 missing
V8numeric43 unique values
0 missing
V9numeric23 unique values
0 missing
V10numeric25 unique values
0 missing
V11numeric15 unique values
0 missing
V12numeric38 unique values
0 missing
V13numeric62 unique values
0 missing
V14numeric17 unique values
0 missing
V15numeric30 unique values
0 missing
V16numeric68 unique values
0 missing
V17numeric6 unique values
0 missing
V18numeric26 unique values
0 missing
V19numeric31 unique values
0 missing
V20numeric155 unique values
0 missing
V21numeric141 unique values
0 missing
V22numeric157 unique values
0 missing
V23numeric71 unique values
0 missing
V24numeric122 unique values
0 missing
V25numeric33 unique values
0 missing
V26numeric157 unique values
0 missing
V27numeric151 unique values
0 missing
V28numeric37 unique values
0 missing
V29numeric28 unique values
0 missing
V30numeric37 unique values
0 missing
V31numeric55 unique values
0 missing
V32numeric33 unique values
0 missing
V33numeric95 unique values
0 missing
V34numeric102 unique values
0 missing
V35numeric55 unique values
0 missing
V36numeric32 unique values
0 missing
V37numeric88 unique values
0 missing
V38numeric97 unique values
0 missing
V39numeric73 unique values
0 missing

62 properties

161
Number of instances (rows) of the dataset.
40
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.
39
Number of numeric attributes.
1
Number of nominal attributes.
0.91
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.
Second quartile (Median) of entropy among attributes.
0.25
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
10.36
Second quartile (Median) of kurtosis 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.
2.59
Mean skewness among attributes of the numeric type.
10.71
Second quartile (Median) of means among attributes of the numeric type.
67.7
Percentage of instances belonging to the most frequent class.
4006.81
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
109
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
3
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.15
Minimum kurtosis among attributes of the numeric type.
2.5
Percentage of binary attributes.
15.14
Second quartile (Median) of standard deviation of attributes of the numeric type.
21.02
Maximum kurtosis among attributes of the numeric type.
0.11
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
54688.74
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
13.26
Third quartile of kurtosis 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.
97.5
Percentage of numeric attributes.
34.92
Third quartile of means among attributes of the numeric type.
2
The maximum number of distinct values among attributes of the nominal type.
-1.35
Minimum skewness among attributes of the numeric type.
2.5
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
4.4
Maximum skewness among attributes of the numeric type.
0.11
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
3.39
Third quartile of skewness among attributes of the numeric type.
145427.38
Maximum standard deviation of attributes of the numeric type.
32.3
Percentage of instances belonging to the least frequent class.
5.2
First quartile of kurtosis among attributes of the numeric type.
40.95
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
52
Number of instances belonging to the least frequent class.
1.89
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.
9.36
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
1526.56
Mean of means among attributes of the numeric type.
2
First quartile of skewness among attributes of the numeric type.
0.68
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
1.04
First quartile of standard deviation of attributes of the numeric type.

12 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
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
Define a new task