Data
credit-approval

credit-approval

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
1 likes downloaded by 27 people , 33 total downloads 0 issues 0 downvotes
  • mythbusting_1 OpenML-CC18 OpenML100 study_1 study_123 study_135 study_14 study_144 study_15 study_20 study_34 study_37 study_41 study_70 study_98 study_99 uci
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Confidential - Donated by Ross Quinlan Source: [UCI](http://archive.ics.uci.edu/ml/datasets/credit+approval) - 1987 Please cite: [UCI](http://archive.ics.uci.edu/ml/citation_policy.html) Credit Approval This file concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect the confidentiality of the data. This dataset is interesting because there is a good mix of attributes -- continuous, nominal with small numbers of values, and nominal with larger numbers of values. There are also a few missing values.

16 features

class (target)nominal2 unique values
0 missing
A1nominal2 unique values
12 missing
A2numeric349 unique values
12 missing
A3numeric215 unique values
0 missing
A4nominal3 unique values
6 missing
A5nominal3 unique values
6 missing
A6nominal14 unique values
9 missing
A7nominal9 unique values
9 missing
A8numeric132 unique values
0 missing
A9nominal2 unique values
0 missing
A10nominal2 unique values
0 missing
A11numeric23 unique values
0 missing
A12nominal2 unique values
0 missing
A13nominal3 unique values
0 missing
A14numeric170 unique values
13 missing
A15numeric240 unique values
0 missing

107 properties

690
Number of instances (rows) of the dataset.
16
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
67
Number of missing values in the dataset.
37
Number of instances with at least one value missing.
6
Number of numeric attributes.
10
Number of nominal attributes.
12.9
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
5
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.71
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
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
4.2
Average number of distinct values among the attributes of the nominal type.
1.4
First quartile of skewness among attributes of the numeric type.
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.88
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
4.05
Standard deviation of the number of distinct values among attributes of the nominal type.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
4.42
Mean skewness among attributes of the numeric type.
4.48
First quartile of standard deviation of attributes of the numeric type.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.14
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
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
901.51
Mean standard deviation of attributes of the numeric type.
0.98
Second quartile (Median) of entropy among attributes.
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.71
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
0.18
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
55.51
Percentage of instances belonging to the most frequent class.
0.5
Minimal entropy among attributes.
15.35
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.99
Entropy of the target attribute values.
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
383
Number of instances belonging to the most frequent class.
1.12
Minimum kurtosis among attributes of the numeric type.
18.16
Second quartile (Median) of means among attributes of the numeric type.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
3.5
Maximum entropy among attributes.
2.22
Minimum of means among attributes of the numeric type.
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.14
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
214.67
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
2.81
Second quartile (Median) of skewness among attributes of the numeric type.
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1017.39
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
31.25
Percentage of binary attributes.
8.47
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.02
Number of attributes divided by the number of instances.
0.43
Maximum mutual information between the nominal attributes and the target attribute.
1.15
Minimum skewness among attributes of the numeric type.
5.36
Percentage of instances having missing values.
1.39
Third quartile of entropy among attributes.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
10.99
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
14
The maximum number of distinct values among attributes of the nominal type.
3.35
Minimum standard deviation of attributes of the numeric type.
0.61
Percentage of missing values.
91.79
Third quartile of kurtosis among attributes of the numeric type.
0.98
Average class difference between consecutive instances.
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
13.14
Maximum skewness among attributes of the numeric type.
44.49
Percentage of instances belonging to the least frequent class.
37.5
Percentage of numeric attributes.
392.36
Third quartile of means among attributes of the numeric type.
0.88
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
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
5210.1
Maximum standard deviation of attributes of the numeric type.
307
Number of instances belonging to the least frequent class.
62.5
Percentage of nominal attributes.
0.13
Third quartile of mutual information between the nominal attributes and the target attribute.
0.14
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
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.25
Average entropy of the attributes.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.82
First quartile of entropy among attributes.
7.15
Third quartile of skewness among attributes of the numeric type.
0.71
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
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
49.93
Mean kurtosis among attributes of the numeric type.
0.22
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.99
First quartile of kurtosis among attributes of the numeric type.
1432.88
Third quartile of standard deviation of attributes of the numeric type.
0.88
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
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
207.06
Mean of means among attributes of the numeric type.
0.09
Average mutual information between the nominal attributes and the target attribute.
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.36
First quartile of means among attributes of the numeric type.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.14
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
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

25 tasks

18491 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
356 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
356 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
192 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
354 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
207 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - 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
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - target_feature: class
1307 runs - target_feature: class
1303 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
Define a new task