Data
Australian

Australian

active ARFF Publicly available Visibility: public Uploaded 04-12-2017 by Jann Goschenhofer
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  • derived OpenML100 study_135 study_144 study_218 study_98
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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

62 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.
6
Number of numeric attributes.
9
Number of nominal attributes.
55.51
Percentage of instances belonging to the most frequent class.
51.82
Mean standard deviation of attributes of the numeric type.
0.04
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
383
Number of instances belonging to the most frequent class.
0.5
Minimal entropy among attributes.
0.69
Second quartile (Median) of skewness among attributes of the numeric type.
3.5
Maximum entropy among attributes.
-0.93
Minimum kurtosis among attributes of the numeric type.
33.33
Percentage of binary attributes.
52.47
Second quartile (Median) of standard deviation of attributes of the numeric type.
4.27
Maximum kurtosis among attributes of the numeric type.
3.3
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
1.59
Third quartile of entropy among attributes.
148.69
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
0.88
Third quartile of kurtosis among attributes of the numeric type.
0.43
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
40
Percentage of numeric attributes.
99.18
Third quartile of means among attributes of the numeric type.
14
The maximum number of distinct values among attributes of the nominal type.
0.39
Minimum skewness among attributes of the numeric type.
60
Percentage of nominal attributes.
0.14
Third quartile of mutual information between the nominal attributes and the target attribute.
2.13
Maximum skewness among attributes of the numeric type.
4.03
Minimum standard deviation of attributes of the numeric type.
0.84
First quartile of entropy among attributes.
1.34
Third quartile of skewness among attributes of the numeric type.
92.93
Maximum standard deviation of attributes of the numeric type.
44.49
Percentage of instances belonging to the least frequent class.
-0.88
First quartile of kurtosis among attributes of the numeric type.
77.75
Third quartile of standard deviation of attributes of the numeric type.
1.31
Average entropy of the attributes.
307
Number of instances belonging to the least frequent class.
28.69
First quartile of means among attributes of the numeric type.
4.15
Standard deviation of the number of distinct values among attributes of the nominal type.
0.19
Mean kurtosis among attributes of the numeric type.
5
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
64.29
Mean of means among attributes of the numeric type.
0.46
First quartile of skewness among attributes of the numeric type.
0.52
Average class difference between consecutive instances.
0.1
Average mutual information between the nominal attributes and the target attribute.
28.26
First quartile of standard deviation of attributes of the numeric type.
0.99
Entropy of the target attribute values.
12.4
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.99
Second quartile (Median) of entropy among attributes.
0.02
Number of attributes divided by the number of instances.
4.22
Average number of distinct values among the attributes of the nominal type.
-0.54
Second quartile (Median) of kurtosis among attributes of the numeric type.
10.14
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.91
Mean skewness among attributes of the numeric type.
56.97
Second quartile (Median) of means among attributes of the numeric type.

5 tasks

4192 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: A15
0 runs - estimation_procedure: 33% Holdout set - target_feature: A15
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: A15
0 runs - estimation_procedure: 50 times Clustering
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