Data
KR-vs-KP-test

KR-vs-KP-test

in_preparation ARFF Publicly available Visibility: public Uploaded 20-06-2017 by Stefan Coors
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Source: Database originally generated and described by Alen Shapiro. Donor/Coder: Rob Holte (holte '@' uottawa.bitnet). The database was supplied to Holte by Peter Clark of the Turing Institute in Glasgow (pete '@' turing.ac.uk). Data Set Information: The dataset format is described below. Note: the format of this database was modified on 2/26/90 to conform with the format of all the other databases in the UCI repository of machine learning databases. Attribute Information: Classes (2): -- White-can-win ("won") and White-cannot-win ("nowin"). I believe that White is deemed to be unable to win if the Black pawn can safely advance. Attributes: see Shapiro's book. #autoxgboost #autoweka

37 features

bkblknominal2 unique values
0 missing
bknwynominal2 unique values
0 missing
bkon8nominal2 unique values
0 missing
bkonanominal2 unique values
0 missing
bksprnominal2 unique values
0 missing
bkxbqnominal2 unique values
0 missing
bkxcrnominal2 unique values
0 missing
bkxwpnominal2 unique values
0 missing
blxwpnominal2 unique values
0 missing
bxqsqnominal2 unique values
0 missing
cntxtnominal2 unique values
0 missing
dsoppnominal2 unique values
0 missing
dwipdnominal2 unique values
0 missing
hdchknominal2 unique values
0 missing
katrinominal3 unique values
0 missing
mulchnominal2 unique values
0 missing
qxmsqnominal2 unique values
0 missing
r2ar8nominal2 unique values
0 missing
reskdnominal2 unique values
0 missing
reskrnominal2 unique values
0 missing
rimmxnominal2 unique values
0 missing
rkxwpnominal2 unique values
0 missing
rxmsqnominal2 unique values
0 missing
simplnominal2 unique values
0 missing
skachnominal2 unique values
0 missing
skewrnominal2 unique values
0 missing
skrxpnominal2 unique values
0 missing
spcopnominal1 unique values
0 missing
stlmtnominal2 unique values
0 missing
thrsknominal2 unique values
0 missing
wkctinominal2 unique values
0 missing
wkna8nominal2 unique values
0 missing
wkncknominal2 unique values
0 missing
wkovlnominal2 unique values
0 missing
wkposnominal2 unique values
0 missing
wtoegnominal2 unique values
0 missing
classnominal2 unique values
0 missing

62 properties

958
Number of instances (rows) of the dataset.
37
Number of attributes (columns) of the dataset.
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.
0
Number of numeric attributes.
37
Number of nominal attributes.
First quartile of skewness among attributes of the numeric type.
Mean of means among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.04
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
94.59
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
100
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
3
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
First quartile of means among attributes of the numeric type.
0.24
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
35
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
Mean kurtosis among attributes of the numeric type.

15 tasks

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
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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