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Australian

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Author: Confidential. Donated by Ross Quinlan Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Statlog+(Australian+Credit+Approval))/LibSVM - 2014-11-14 Please cite: This is the famous Australian dataset, retrieved 2014-11-14 from the libSVM site. It was normalized. The original version is from [UCI](https://archive.ics.uci.edu/ml/datasets/Statlog+(Australian+Credit+Approval)). 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. Source: Statlog / Australian # of classes: 2 # of data: 690 # of features: 14

15 features

Y (target)nominal2 unique values
0 missing
X1numeric2 unique values
0 missing
X2numeric350 unique values
0 missing
X3numeric215 unique values
0 missing
X4numeric3 unique values
0 missing
X5numeric14 unique values
0 missing
X6numeric8 unique values
0 missing
X7numeric132 unique values
0 missing
X8numeric2 unique values
0 missing
X9numeric2 unique values
0 missing
X10numeric23 unique values
0 missing
X11numeric2 unique values
0 missing
X12numeric3 unique values
0 missing
X13numeric171 unique values
0 missing
X14numeric240 unique values
0 missing

107 properties

690
Number of instances (rows) of the dataset.
15
Number of attributes (columns) of the dataset.
2
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.
14
Number of numeric attributes.
1
Number of nominal attributes.
1
Maximum standard deviation of attributes of the numeric type.
44.49
Percentage of instances belonging to the least frequent class.
93.33
Percentage of numeric attributes.
-0.06
Third quartile of means among attributes of the numeric type.
0.84
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
Average entropy of the attributes.
307
Number of instances belonging to the least frequent class.
6.67
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.15
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
0.23
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
21.3
Mean kurtosis among attributes of the numeric type.
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
2.79
Third quartile of skewness among attributes of the numeric type.
0.69
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
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.84
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
-0.35
Mean of means among attributes of the numeric type.
0.28
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.54
First quartile of kurtosis among attributes of the numeric type.
0.95
Third quartile of standard deviation of attributes of the numeric type.
0.84
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
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.82
First quartile of means among attributes of the numeric type.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.15
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
0.23
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
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
0.69
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
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
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
2
Average number of distinct values among the attributes of the nominal type.
-0.26
First quartile of skewness among attributes of the numeric type.
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.84
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
0.82
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
1.68
Mean skewness among attributes of the numeric type.
0.22
First quartile of standard deviation of attributes of the numeric type.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.15
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
0.18
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
55.51
Percentage of instances belonging to the most frequent class.
0.51
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.69
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
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
383
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.51
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.
Maximum entropy among attributes.
-2
Minimum kurtosis among attributes of the numeric type.
-0.19
Second quartile (Median) of means among attributes of the numeric type.
0.86
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
214.67
Maximum kurtosis among attributes of the numeric type.
-0.98
Minimum of means among attributes of the numeric type.
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
0.36
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.38
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
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
6.67
Percentage of binary attributes.
0.39
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.
2
The maximum number of distinct values among attributes of the nominal type.
-1.94
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.23
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
13.14
Maximum skewness among attributes of the numeric type.
0.1
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
13.38
Third quartile of kurtosis among attributes of the numeric type.
0.52
Average class difference between consecutive instances.
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

11 tasks

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