Data
BNG(anneal.ORIG,10000,10)

BNG(anneal.ORIG,10000,10)

active ARFF public domain Visibility: public Uploaded 22-02-2015 by Jan van Rijn
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39 features

class (target)nominal6 unique values
0 missing
familynominal9 unique values
0 missing
product-typenominal3 unique values
0 missing
steelnominal8 unique values
0 missing
carbonnumeric281770 unique values
0 missing
hardnessnumeric218738 unique values
0 missing
temper_rollingnominal1 unique values
0 missing
conditionnominal3 unique values
0 missing
formabilitynominal5 unique values
0 missing
strengthnumeric305252 unique values
0 missing
non-ageingnominal1 unique values
0 missing
surface-finishnominal2 unique values
0 missing
surface-qualitynominal4 unique values
0 missing
enamelabilitynominal5 unique values
0 missing
bcnominal1 unique values
0 missing
bfnominal1 unique values
0 missing
btnominal1 unique values
0 missing
bw%2Fmenominal2 unique values
0 missing
blnominal1 unique values
0 missing
mnominal1 unique values
0 missing
chromnominal1 unique values
0 missing
phosnominal1 unique values
0 missing
cbondnominal1 unique values
0 missing
marvinominal1 unique values
0 missing
exptlnominal1 unique values
0 missing
ferronominal1 unique values
0 missing
corrnominal1 unique values
0 missing
blue%2Fbright%2Fvarn%2Fcleannominal4 unique values
0 missing
lustrenominal1 unique values
0 missing
jurofmnominal1 unique values
0 missing
snominal1 unique values
0 missing
pnominal1 unique values
0 missing
shapenominal2 unique values
0 missing
thicknumeric737724 unique values
0 missing
widthnumeric595545 unique values
0 missing
lennumeric224385 unique values
0 missing
oilnominal2 unique values
0 missing
borenominal4 unique values
0 missing
packingnominal3 unique values
0 missing

62 properties

1000000
Number of instances (rows) of the dataset.
39
Number of attributes (columns) of the dataset.
6
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.
33
Number of nominal attributes.
884.58
Third quartile of means among attributes of the numeric type.
0.08
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
15.38
Percentage of numeric attributes.
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
9
The maximum number of distinct values among attributes of the nominal type.
0.06
Minimum skewness among attributes of the numeric type.
84.62
Percentage of nominal attributes.
1.37
Third quartile of skewness among attributes of the numeric type.
1.4
Maximum skewness among attributes of the numeric type.
0.9
Minimum standard deviation of attributes of the numeric type.
0
First quartile of entropy among attributes.
758.51
Third quartile of standard deviation of attributes of the numeric type.
1810.23
Maximum standard deviation of attributes of the numeric type.
0.06
Percentage of instances belonging to the least frequent class.
-1.28
First quartile of kurtosis among attributes of the numeric type.
2.14
Standard deviation of the number of distinct values among attributes of the nominal type.
0.53
Average entropy of the attributes.
555
Number of instances belonging to the least frequent class.
12.54
First quartile of means among attributes of the numeric type.
-0.45
Mean kurtosis among attributes of the numeric type.
4
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
367.05
Mean of means among attributes of the numeric type.
0.4
First quartile of skewness among attributes of the numeric type.
0.6
Average class difference between consecutive instances.
0.01
Average mutual information between the nominal attributes and the target attribute.
19.41
First quartile of standard deviation of attributes of the numeric type.
1.2
Entropy of the target attribute values.
39.57
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
2.42
Average number of distinct values among the attributes of the nominal type.
-0.73
Second quartile (Median) of kurtosis among attributes of the numeric type.
92.87
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.85
Mean skewness among attributes of the numeric type.
108.27
Second quartile (Median) of means among attributes of the numeric type.
75.97
Percentage of instances belonging to the most frequent class.
418.34
Mean standard deviation of attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
759652
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
0.86
Second quartile (Median) of skewness among attributes of the numeric type.
2.47
Maximum entropy among attributes.
-1.32
Minimum kurtosis among attributes of the numeric type.
10.26
Percentage of binary attributes.
132.69
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.95
Third quartile of entropy among attributes.
1.17
Maximum kurtosis among attributes of the numeric type.
1.25
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
0.42
Third quartile of kurtosis among attributes of the numeric type.
1183.17
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.

12 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
28 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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