Data
KC3

KC3

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
V1numeric25 unique values
0 missing
V2numeric35 unique values
0 missing
V3numeric34 unique values
0 missing
V4numeric6 unique values
0 missing
V5numeric20 unique values
0 missing
V6numeric29 unique values
0 missing
V7numeric24 unique values
0 missing
V8numeric34 unique values
0 missing
V9numeric16 unique values
0 missing
V10numeric21 unique values
0 missing
V11numeric21 unique values
0 missing
V12numeric27 unique values
0 missing
V13numeric87 unique values
0 missing
V14numeric13 unique values
0 missing
V15numeric26 unique values
0 missing
V16numeric74 unique values
0 missing
V17numeric4 unique values
0 missing
V18numeric22 unique values
0 missing
V19numeric31 unique values
0 missing
V20numeric291 unique values
0 missing
V21numeric223 unique values
0 missing
V22numeric297 unique values
0 missing
V23numeric77 unique values
0 missing
V24numeric177 unique values
0 missing
V25numeric33 unique values
0 missing
V26numeric296 unique values
0 missing
V27numeric276 unique values
0 missing
V28numeric37 unique values
0 missing
V29numeric18 unique values
0 missing
V30numeric29 unique values
0 missing
V31numeric77 unique values
0 missing
V32numeric34 unique values
0 missing
V33numeric116 unique values
0 missing
V34numeric147 unique values
0 missing
V35numeric70 unique values
0 missing
V36numeric29 unique values
0 missing
V37numeric89 unique values
0 missing
V38numeric87 unique values
0 missing
V39numeric77 unique values
0 missing

62 properties

458
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.
2.57
First quartile of skewness among attributes of the numeric type.
395.53
Mean of means among attributes of the numeric type.
0.57
First quartile of standard deviation of attributes of the numeric type.
0.91
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
0.45
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.
11.67
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.09
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
3.45
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.
3.18
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
90.61
Percentage of instances belonging to the most frequent class.
1244.97
Mean standard deviation of attributes of the numeric type.
3.08
Second quartile (Median) of skewness among attributes of the numeric type.
415
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
2.5
Percentage of binary attributes.
5.8
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.32
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
135.62
Maximum kurtosis among attributes of the numeric type.
0.1
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
20.6
Third quartile of kurtosis among attributes of the numeric type.
13728.24
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
97.5
Percentage of numeric attributes.
17.14
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.
2.5
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.
-2.02
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
3.78
Third quartile of skewness among attributes of the numeric type.
10.03
Maximum skewness among attributes of the numeric type.
0.13
Minimum standard deviation of attributes of the numeric type.
7.27
First quartile of kurtosis among attributes of the numeric type.
26.47
Third quartile of standard deviation of attributes of the numeric type.
44394.87
Maximum standard deviation of attributes of the numeric type.
9.39
Percentage of instances belonging to the least frequent class.
43
Number of instances belonging to the least frequent class.
0.93
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.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
19.27
Mean kurtosis among attributes of the numeric type.

9 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - 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
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