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Author: Source: Unknown - Please cite: 1. Title: Credit Approval 2. Sources: (confidential) Submitted by quinlan@cs.su.oz.au 3. Past Usage: See Quinlan, * "Simplifying decision trees", Int J Man-Machine Studies 27, Dec 1987, pp. 221-234. * "C4.5: Programs for Machine Learning", Morgan Kaufmann, Oct 1992 4. Relevant Information: This file concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect 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. 5. Number of Instances: 690 6. Number of Attributes: 15 + class attribute 7. Attribute Information: A1: b, a. A2: continuous. A3: continuous. A4: u, y, l, t. A5: g, p, gg. A6: c, d, cc, i, j, k, m, r, q, w, x, e, aa, ff. A7: v, h, bb, j, n, z, dd, ff, o. A8: continuous. A9: t, f. A10: t, f. A11: continuous. A12: t, f. A13: g, p, s. A14: continuous. A15: continuous. A16: +,- (class attribute) 8. Missing Attribute Values: 37 cases (5%) have one or more missing values. The missing values from particular attributes are: A1: 12 A2: 12 A4: 6 A5: 6 A6: 9 A7: 9 A14: 13 9. Class Distribution +: 307 (44.5%) -: 383 (55.5%)

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
Show all 16 features

107 properties

NumberOfInstances
690
Number of instances (rows) of the dataset.
NumberOfFeatures
16
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
67
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
37
Number of instances with at least one value missing.
NumberOfNumericFeatures
6
Number of numeric attributes.
NumberOfSymbolicFeatures
10
Number of nominal attributes.
AutoCorrelation
0.98
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
0.99
Entropy of the target attribute values.
DecisionStumpAUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.02
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
10.99
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
55.51
Percentage of instances belonging to the most frequent class.
MajorityClassSize
383
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
3.5
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
214.67
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
1017.39
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.43
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
14
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
13.14
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
5210.1
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.25
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
49.93
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
207.06
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.09
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
12.9
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
4.2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
4.42
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
901.51
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0.5
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
1.12
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
2.22
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
1.15
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
3.35
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
44.49
Percentage of instances belonging to the least frequent class.
MinorityClassSize
307
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.22
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
5
Number of binary attributes.
PercentageOfBinaryFeatures
31.25
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
5.36
Percentage of instances having missing values.
PercentageOfMissingValues
0.61
Percentage of missing values.
PercentageOfNumericFeatures
37.5
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
62.5
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.82
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
1.99
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
2.36
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
1.4
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
4.48
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
0.98
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
15.35
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
18.16
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
2.81
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
8.47
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
1.39
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
91.79
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
392.36
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.13
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
7.15
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
1432.88
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
4.05
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

11 tasks

Supervised Classification on credit-a
106 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on credit-a
0 runs - estimation_procedure: Test on Training Data - target_feature: class
Supervised Classification on credit-a
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
Supervised Classification on credit-a
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on credit-a
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
Supervised Classification on credit-a
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
Supervised Classification on credit-a
0 runs - estimation_procedure: Leave one out - target_feature: class
Supervised Classification on credit-a
0 runs - estimation_procedure: 10% Holdout set - target_feature: class
Learning Curve on credit-a
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
Learning Curve on credit-a
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on credit-a
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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