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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

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

11 tasks

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