Data
Australian

Australian

deactivated ARFF Publicly available Visibility: public Uploaded 21-09-2016 by Joaquin Vanschoren
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Author: Confidential. Donated by Ross Quinlan Source: [LibSVM] (https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html), [UCI](https://archive.ics.uci.edu/ml/datasets/Statlog+(Australian+Credit+Approval)) - 1987 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) Australian Credit Approval. This is the famous Australian Credit Approval dataset, originating from the StatLog project. It concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect the confidentiality of the data. This dataset was retrieved 2014-11-14 from the libSVM site. It was normalized to [-1,1] and converted to the ARFF format. ### Feature information There are 6 numerical and 8 categorical attributes, all normalized to [-1,1]. The original formatting was as follows: A1: 0,1 CATEGORICAL (formerly: a,b) A2: continuous. A3: continuous. A4: 1,2,3 CATEGORICAL (formerly: p,g,gg) A5: 1, 2,3,4,5, 6,7,8,9,10,11,12,13,14 CATEGORICAL (formerly: ff,d,i,k,j,aa,m,c,w, e, q, r,cc, x) A6: 1, 2,3, 4,5,6,7,8,9 CATEGORICAL (formerly: ff,dd,j,bb,v,n,o,h,z) A7: continuous. A8: 1, 0 CATEGORICAL (formerly: t, f) A9: 1, 0 CATEGORICAL (formerly: t, f) A10: continuous. A11: 1, 0 CATEGORICAL (formerly t, f) A12: 1, 2, 3 CATEGORICAL (formerly: s, g, p) A13: continuous. A14: continuous. A15: 1,2 class attribute (formerly: +,-) ### Relevant Papers Ross Quinlan. "Simplifying decision trees", Int J Man-Machine Studies 27, Dec 1987, pp. 221-234. Ross Quinlan. "C4.5: Programs for Machine Learning", Morgan Kaufmann, Oct 1992

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
Show all 15 features

62 properties

NumberOfInstances
690
Number of instances (rows) of the dataset.
NumberOfFeatures
15
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
14
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
ClassEntropy
0.99
Entropy of the target attribute values.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Dimensionality
0.02
Number of attributes divided by the number of instances.
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
Quartile2KurtosisOfNumericAtts
0.51
Second quartile (Median) of kurtosis among attributes of the numeric type.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MeanSkewnessOfNumericAtts
1.68
Mean skewness among attributes of the numeric type.
Quartile2MeansOfNumericAtts
-0.19
Second quartile (Median) of means among attributes of the numeric type.
MajorityClassPercentage
55.51
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
0.51
Mean standard deviation of attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
MajorityClassSize
383
Number of instances belonging to the most frequent class.
MinAttributeEntropy
Minimal entropy among attributes.
Quartile2SkewnessOfNumericAtts
0.38
Second quartile (Median) of skewness among attributes of the numeric type.
MaxAttributeEntropy
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
-2
Minimum kurtosis among attributes of the numeric type.
PercentageOfBinaryFeatures
6.67
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
0.39
Second quartile (Median) of standard deviation of attributes of the numeric type.
MaxKurtosisOfNumericAtts
214.67
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-0.98
Minimum of means among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
MaxMeansOfNumericAtts
0.36
Maximum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
13.38
Third quartile of kurtosis among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
PercentageOfNumericFeatures
93.33
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
-0.06
Third quartile of means among attributes of the numeric type.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1.94
Minimum skewness among attributes of the numeric type.
PercentageOfSymbolicFeatures
6.67
Percentage of nominal attributes.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
MaxSkewnessOfNumericAtts
13.14
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.1
Minimum standard deviation of attributes of the numeric type.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
2.79
Third quartile of skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
1
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
44.49
Percentage of instances belonging to the least frequent class.
Quartile1KurtosisOfNumericAtts
-1.54
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.95
Third quartile of standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MinorityClassSize
307
Number of instances belonging to the least frequent class.
Quartile1MeansOfNumericAtts
-0.82
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
MeanKurtosisOfNumericAtts
21.3
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
1
Number of binary attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
MeanMeansOfNumericAtts
-0.35
Mean of means among attributes of the numeric type.
Quartile1SkewnessOfNumericAtts
-0.26
First quartile of skewness among attributes of the numeric type.
AutoCorrelation
0.52
Average class difference between consecutive instances.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
Quartile1StdDevOfNumericAtts
0.22
First quartile of standard deviation of attributes of the numeric type.
Show all 62 properties

15 tasks

Supervised Classification on Australian
208614 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Y
Supervised Classification on Australian
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Y
Learning Curve on Australian
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Y
Clustering on Australian
0 runs - estimation_procedure: 50 times Clustering
Clustering on Australian
0 runs - estimation_procedure: 50 times Clustering
Clustering on Australian
0 runs - estimation_procedure: 50 times Clustering
Clustering on Australian
0 runs - estimation_procedure: 50 times Clustering
Clustering on Australian
0 runs - estimation_procedure: 50 times Clustering
Clustering on Australian
0 runs
Clustering on Australian
0 runs - target_feature: Y
Clustering on Australian
0 runs - estimation_procedure: 50 times Clustering
Clustering on Australian
0 runs - estimation_procedure: 50 times Clustering
Clustering on Australian
0 runs - estimation_procedure: 50 times Clustering
Clustering on Australian
0 runs - estimation_procedure: 50 times Clustering
Clustering on Australian
0 runs - estimation_procedure: 50 times Clustering
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