Data
TestingDataSet

TestingDataSet

in_preparation ARFF Publicly available Visibility: public Uploaded 23-08-2017 by Sami Ullah
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
TestingOpenmlDataset

39 features

class (target)nominal5 unique values
0 missing
familynominal2 unique values
772 missing
product-typenominal1 unique values
0 missing
steelnominal7 unique values
86 missing
carbonnumeric10 unique values
0 missing
hardnessnumeric7 unique values
0 missing
temper_rollingnominal1 unique values
761 missing
conditionnominal2 unique values
303 missing
formabilitynominal4 unique values
318 missing
strengthnumeric8 unique values
0 missing
non-ageingnominal1 unique values
793 missing
surface-finishnominal1 unique values
889 missing
surface-qualitynominal4 unique values
244 missing
enamelabilitynominal2 unique values
882 missing
bcnominal1 unique values
897 missing
bfnominal1 unique values
769 missing
btnominal1 unique values
824 missing
bw%2Fmenominal2 unique values
687 missing
blnominal1 unique values
749 missing
mnominal0 unique values
898 missing
chromnominal1 unique values
872 missing
phosnominal1 unique values
891 missing
cbondnominal1 unique values
824 missing
marvinominal0 unique values
898 missing
exptlnominal1 unique values
896 missing
ferronominal1 unique values
868 missing
corrnominal0 unique values
898 missing
blue%2Fbright%2Fvarn%2Fcleannominal3 unique values
892 missing
lustrenominal1 unique values
847 missing
jurofmnominal0 unique values
898 missing
snominal0 unique values
898 missing
pnominal0 unique values
898 missing
shapenominal2 unique values
0 missing
thicknumeric50 unique values
0 missing
widthnumeric68 unique values
0 missing
lennumeric24 unique values
0 missing
oilnominal2 unique values
834 missing
borenominal3 unique values
0 missing
packingnominal2 unique values
889 missing

62 properties

898
Number of instances (rows) of the dataset.
39
Number of attributes (columns) of the dataset.
5
Number of distinct values of the target attribute (if it is nominal).
22175
Number of missing values in the dataset.
898
Number of instances with at least one value missing.
6
Number of numeric attributes.
33
Number of nominal attributes.
0.97
First quartile of skewness among attributes of the numeric type.
348.5
Mean of means among attributes of the numeric type.
10.51
First quartile of standard deviation of attributes of the numeric type.
0.61
Average class difference between consecutive instances.
0.04
Average mutual information between the nominal attributes and the target attribute.
0
Second quartile (Median) of entropy among attributes.
1.19
Entropy of the target attribute values.
4.67
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.64
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.04
Number of attributes divided by the number of instances.
1.64
Average number of distinct values among the attributes of the nominal type.
21.22
Second quartile (Median) of means among attributes of the numeric type.
26.84
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2.03
Mean skewness among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
76.17
Percentage of instances belonging to the most frequent class.
405.17
Mean standard deviation of attributes of the numeric type.
1.65
Second quartile (Median) of skewness among attributes of the numeric type.
684
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
10.26
Percentage of binary attributes.
69.85
Second quartile (Median) of standard deviation of attributes of the numeric type.
1.82
Maximum entropy among attributes.
-0.97
Minimum kurtosis among attributes of the numeric type.
100
Percentage of instances having missing values.
0.24
Third quartile of entropy among attributes.
13.22
Maximum kurtosis among attributes of the numeric type.
1.2
Minimum of means among attributes of the numeric type.
63.32
Percentage of missing values.
12.74
Third quartile of kurtosis among attributes of the numeric type.
1263.09
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
15.38
Percentage of numeric attributes.
901.26
Third quartile of means among attributes of the numeric type.
0.41
Maximum mutual information between the nominal attributes and the target attribute.
0
The minimal number of distinct values among attributes of the nominal type.
84.62
Percentage of nominal attributes.
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
7
The maximum number of distinct values among attributes of the nominal type.
0.07
Minimum skewness among attributes of the numeric type.
0
First quartile of entropy among attributes.
3.75
Third quartile of skewness among attributes of the numeric type.
3.76
Maximum skewness among attributes of the numeric type.
0.87
Minimum standard deviation of attributes of the numeric type.
-0.4
First quartile of kurtosis among attributes of the numeric type.
771.86
Third quartile of standard deviation of attributes of the numeric type.
1871.4
Maximum standard deviation of attributes of the numeric type.
0.89
Percentage of instances belonging to the least frequent class.
8
Number of instances belonging to the least frequent class.
3.03
First quartile of means among attributes of the numeric type.
1.56
Standard deviation of the number of distinct values among attributes of the nominal type.
0.25
Average entropy of the attributes.
4
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
4.65
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
Define a new task