Data
led24

led24

active ARFF public Visibility: public Uploaded 06-04-2017 by Pieter Gijsbers
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led24-pmlb

25 features

class (target)nominal10 unique values
0 missing
attribute#1nominal2 unique values
0 missing
attribute#2nominal2 unique values
0 missing
attribute#3nominal2 unique values
0 missing
attribute#4nominal2 unique values
0 missing
attribute#5nominal2 unique values
0 missing
attribute#6nominal2 unique values
0 missing
attribute#7nominal2 unique values
0 missing
irrelevant1nominal2 unique values
0 missing
irrelevant2nominal2 unique values
0 missing
irrelevant3nominal2 unique values
0 missing
irrelevant4nominal2 unique values
0 missing
irrelevant5nominal2 unique values
0 missing
irrelevant6nominal2 unique values
0 missing
irrelevant7nominal2 unique values
0 missing
irrelevant8nominal2 unique values
0 missing
irrelevant9nominal2 unique values
0 missing
irrelevant10nominal2 unique values
0 missing
irrelevant11nominal2 unique values
0 missing
irrelevant12nominal2 unique values
0 missing
irrelevant13nominal2 unique values
0 missing
irrelevant14nominal2 unique values
0 missing
irrelevant15nominal2 unique values
0 missing
irrelevant16nominal2 unique values
0 missing
irrelevant17nominal2 unique values
0 missing

62 properties

3200
Number of instances (rows) of the dataset.
25
Number of attributes (columns) of the dataset.
10
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.
25
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.
0.1
Average class difference between consecutive instances.
0.12
Average mutual information between the nominal attributes and the target attribute.
1
Second quartile (Median) of entropy among attributes.
3.32
Entropy of the target attribute values.
7.08
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.
2.32
Average number of distinct values among the attributes of the nominal type.
Second quartile (Median) of means among attributes of the numeric type.
27.8
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
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
10.53
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.
337
Number of instances belonging to the most frequent class.
0.69
Minimal entropy among attributes.
96
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.
1
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.
0.52
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.
0.32
Third quartile of mutual information between the nominal attributes and the target attribute.
10
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
0.98
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.
9.25
Percentage of instances belonging to the least frequent class.
296
Number of instances belonging to the least frequent class.
First quartile of means among attributes of the numeric type.
1.6
Standard deviation of the number of distinct values among attributes of the nominal type.
0.97
Average entropy of the attributes.
24
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.

12 tasks

31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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