Data
Australian

Australian

active ARFF Publicly available Visibility: public Uploaded 04-12-2017 by Jann Goschenhofer
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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) Important note: This dataset is derived from [credit-approval](https://www.openml.org/d/29), even though both datasets exist individually on UCI. In this version, missing values were filled in (not clear how) and a duplicate feature was removed. 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 UCI site and converted to the ARFF format. __Major changes w.r.t. version 3: dataset from UCI that matches description and data types__ ### 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

A15 (target)nominal2 unique values
0 missing
A1nominal2 unique values
0 missing
A2numeric350 unique values
0 missing
A3numeric215 unique values
0 missing
A4nominal3 unique values
0 missing
A5nominal14 unique values
0 missing
A6nominal8 unique values
0 missing
A7numeric132 unique values
0 missing
A8nominal2 unique values
0 missing
A9nominal2 unique values
0 missing
A10numeric23 unique values
0 missing
A11nominal2 unique values
0 missing
A12nominal3 unique values
0 missing
A13numeric171 unique values
0 missing
A14numeric240 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
6
Number of numeric attributes.
NumberOfSymbolicFeatures
9
Number of nominal attributes.
MaxMutualInformation
0.43
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
40
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
99.18
Third quartile of means among attributes of the numeric type.
MaxNominalAttDistinctValues
14
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
0.39
Minimum skewness among attributes of the numeric type.
PercentageOfSymbolicFeatures
60
Percentage of nominal attributes.
Quartile3MutualInformation
0.14
Third quartile of mutual information between the nominal attributes and the target attribute.
MaxSkewnessOfNumericAtts
2.13
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
4.03
Minimum standard deviation of attributes of the numeric type.
Quartile1AttributeEntropy
0.84
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
1.34
Third quartile of skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
92.93
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
44.49
Percentage of instances belonging to the least frequent class.
Quartile1KurtosisOfNumericAtts
-0.88
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
77.75
Third quartile of standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.31
Average entropy of the attributes.
MinorityClassSize
307
Number of instances belonging to the least frequent class.
Quartile1MeansOfNumericAtts
28.69
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
4.15
Standard deviation of the number of distinct values among attributes of the nominal type.
MeanKurtosisOfNumericAtts
0.19
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
5
Number of binary attributes.
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
MeanMeansOfNumericAtts
64.29
Mean of means among attributes of the numeric type.
Quartile1SkewnessOfNumericAtts
0.46
First quartile of skewness among attributes of the numeric type.
MeanMutualInformation
0.1
Average mutual information between the nominal attributes and the target attribute.
Quartile1StdDevOfNumericAtts
28.26
First quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.52
Average class difference between consecutive instances.
MeanNoiseToSignalRatio
12.4
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Quartile2AttributeEntropy
0.99
Second quartile (Median) of entropy among attributes.
ClassEntropy
0.99
Entropy of the target attribute values.
MeanNominalAttDistinctValues
4.22
Average number of distinct values among the attributes of the nominal type.
Quartile2KurtosisOfNumericAtts
-0.54
Second quartile (Median) of kurtosis among attributes of the numeric type.
Dimensionality
0.02
Number of attributes divided by the number of instances.
MeanSkewnessOfNumericAtts
0.91
Mean skewness among attributes of the numeric type.
Quartile2MeansOfNumericAtts
56.97
Second quartile (Median) of means among attributes of the numeric type.
EquivalentNumberOfAtts
10.14
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MeanStdDevOfNumericAtts
51.82
Mean standard deviation of attributes of the numeric type.
Quartile2MutualInformation
0.04
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
MajorityClassPercentage
55.51
Percentage of instances belonging to the most frequent class.
MinAttributeEntropy
0.5
Minimal entropy among attributes.
Quartile2SkewnessOfNumericAtts
0.69
Second quartile (Median) of skewness among attributes of the numeric type.
MajorityClassSize
383
Number of instances belonging to the most frequent class.
MinKurtosisOfNumericAtts
-0.93
Minimum kurtosis among attributes of the numeric type.
PercentageOfBinaryFeatures
33.33
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
52.47
Second quartile (Median) of standard deviation of attributes of the numeric type.
MaxAttributeEntropy
3.5
Maximum entropy among attributes.
MinMeansOfNumericAtts
3.3
Minimum of means among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
1.59
Third quartile of entropy among attributes.
MaxKurtosisOfNumericAtts
4.27
Maximum kurtosis among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
0.88
Third quartile of kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
148.69
Maximum of means among attributes of the numeric type.
Show all 62 properties

16 tasks

Supervised Classification on Australian
4202 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: A15
Supervised Classification on Australian
5 runs - estimation_procedure: 33% Holdout set - target_feature: A15
Supervised Classification on Australian
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: A15
Supervised Classification on Australian
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: A15
Supervised Classification on Australian
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: A15
Supervised Classification on Australian
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: classification problem
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 - 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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