Data
Australian

Australian

active ARFF Publicly available Visibility: public Uploaded 21-09-2016 by Joaquin Vanschoren
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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.
0.52
Average class difference between consecutive instances.
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
0.15
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
0.69
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
0.84
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
0.15
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
0.69
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
0.84
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
0.15
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
0.69
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
0.99
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.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.84
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
0.84
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
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.84
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.
Maximum entropy among attributes.
214.67
Maximum kurtosis among attributes of the numeric type.
0.36
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
13.14
Maximum skewness among attributes of the numeric type.
1
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
21.3
Mean kurtosis among attributes of the numeric type.
-0.35
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.
2
Average number of distinct values among the attributes of the nominal type.
1.68
Mean skewness among attributes of the numeric type.
0.51
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-2
Minimum kurtosis among attributes of the numeric type.
-0.98
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
-1.94
Minimum skewness among attributes of the numeric type.
0.1
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.
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.28
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1
Number of binary attributes.
6.67
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
93.33
Percentage of numeric attributes.
6.67
Percentage of nominal attributes.
First quartile of entropy among attributes.
-1.54
First quartile of kurtosis among attributes of the numeric type.
-0.82
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
-0.26
First quartile of skewness among attributes of the numeric type.
0.22
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.51
Second quartile (Median) of kurtosis among attributes of the numeric type.
-0.19
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.
0.38
Second quartile (Median) of skewness among attributes of the numeric type.
0.39
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
13.38
Third quartile of kurtosis among attributes of the numeric type.
-0.06
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
2.79
Third quartile of skewness among attributes of the numeric type.
0.95
Third 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 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
0.86
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
0.86
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
0.23
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.54
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
0.23
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.54
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
0.23
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
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.
0.82
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

0 runs - estimation_procedure: Test on Training Data - target_feature: Y
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Y
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Y
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Y
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Y
0 runs - estimation_procedure: Leave one out - target_feature: Y
0 runs - estimation_procedure: 10% Holdout set - target_feature: Y
0 runs - estimation_procedure: 33% Holdout set - 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
Define a new task