Data
pol

pol

active ARFF Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Source: Unknown - Please cite: This is a commercial application described in Weiss & Indurkhya (1995). The data describes a telecommunication problem. No further information is available. Characteristics: (10000+5000) cases, 49 continuous attributes Source: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at http://www.ncc.up.pt/~ltorgo/Regression/DataSets.html Original Source: The data in the original format can be obtained from http://www.cs.su.oz.au/~nitin

49 features

foo (target)numeric11 unique values
0 missing
f26numeric68 unique values
0 missing
f25numeric68 unique values
0 missing
f27numeric65 unique values
0 missing
f28numeric64 unique values
0 missing
f29numeric62 unique values
0 missing
f30numeric44 unique values
0 missing
f31numeric43 unique values
0 missing
f32numeric42 unique values
0 missing
f33numeric38 unique values
0 missing
f34numeric1 unique values
0 missing
f35numeric1 unique values
0 missing
f36numeric1 unique values
0 missing
f37numeric1 unique values
0 missing
f38numeric1 unique values
0 missing
f39numeric1 unique values
0 missing
f40numeric1 unique values
0 missing
f41numeric1 unique values
0 missing
f42numeric1 unique values
0 missing
f43numeric1 unique values
0 missing
f44numeric1 unique values
0 missing
f45numeric1 unique values
0 missing
f46numeric1 unique values
0 missing
f47numeric1 unique values
0 missing
f48numeric1 unique values
0 missing
f13numeric97 unique values
0 missing
f2numeric1 unique values
0 missing
f3numeric1 unique values
0 missing
f4numeric1 unique values
0 missing
f5numeric184 unique values
0 missing
f6numeric118 unique values
0 missing
f7numeric114 unique values
0 missing
f8numeric106 unique values
0 missing
f9numeric80 unique values
0 missing
f10numeric1 unique values
0 missing
f11numeric1 unique values
0 missing
f12numeric1 unique values
0 missing
f1numeric1 unique values
0 missing
f14numeric117 unique values
0 missing
f15numeric121 unique values
0 missing
f16numeric120 unique values
0 missing
f17numeric120 unique values
0 missing
f18numeric123 unique values
0 missing
f19numeric102 unique values
0 missing
f20numeric86 unique values
0 missing
f21numeric85 unique values
0 missing
f22numeric88 unique values
0 missing
f23numeric79 unique values
0 missing
f24numeric63 unique values
0 missing

107 properties

15000
Number of instances (rows) of the dataset.
49
Number of attributes (columns) of the dataset.
0
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.
49
Number of numeric attributes.
0
Number of nominal attributes.
-39.42
Average class difference between consecutive instances.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Entropy of the target attribute values.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Number of attributes divided by the number of instances.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
147.26
Maximum kurtosis among attributes of the numeric type.
110
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
11.62
Maximum skewness among attributes of the numeric type.
41.73
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
33.56
Mean kurtosis among attributes of the numeric type.
19.63
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Average number of distinct values among the attributes of the nominal type.
4.64
Mean skewness among attributes of the numeric type.
7.24
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-0.99
Minimum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
0.31
Minimum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
100
Percentage of numeric attributes.
0
Percentage of nominal attributes.
First quartile of entropy among attributes.
3.33
First quartile of kurtosis among attributes of the numeric type.
0
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
1.86
First quartile of skewness among attributes of the numeric type.
0
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
14.54
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.94
Second quartile (Median) of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
3.67
Second quartile (Median) of skewness among attributes of the numeric type.
3.15
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
69.44
Third quartile of kurtosis among attributes of the numeric type.
12.1
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
7.78
Third quartile of skewness among attributes of the numeric type.
12.46
Third quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Standard deviation of the number of distinct values among attributes of the nominal type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

7 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: foo
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: foo
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: foo
0 runs - estimation_procedure: Leave one out - target_feature: foo
0 runs - estimation_procedure: 10% Holdout set - target_feature: foo
0 runs - estimation_procedure: 33% Holdout set - target_feature: foo
0 runs - estimation_procedure: Test on Training Data - target_feature: foo
Define a new task