Data
BNG(hypothyroid,nominal,1000000)

BNG(hypothyroid,nominal,1000000)

active ARFF Publicly available Visibility: public Uploaded 09-04-2014 by Jan van Rijn
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30 features

Class (target)nominal4 unique values
0 missing
agenominal3 unique values
0 missing
sexnominal2 unique values
0 missing
on_thyroxinenominal2 unique values
0 missing
query_on_thyroxinenominal2 unique values
0 missing
on_antithyroid_medicationnominal2 unique values
0 missing
sicknominal2 unique values
0 missing
pregnantnominal2 unique values
0 missing
thyroid_surgerynominal2 unique values
0 missing
I131_treatmentnominal2 unique values
0 missing
query_hypothyroidnominal2 unique values
0 missing
query_hyperthyroidnominal2 unique values
0 missing
lithiumnominal2 unique values
0 missing
goitrenominal2 unique values
0 missing
tumornominal2 unique values
0 missing
hypopituitarynominal2 unique values
0 missing
psychnominal2 unique values
0 missing
TSH_measurednominal2 unique values
0 missing
TSHnominal3 unique values
0 missing
T3_measurednominal2 unique values
0 missing
T3nominal3 unique values
0 missing
TT4_measurednominal2 unique values
0 missing
TT4nominal3 unique values
0 missing
T4U_measurednominal2 unique values
0 missing
T4Unominal3 unique values
0 missing
FTI_measurednominal2 unique values
0 missing
FTInominal3 unique values
0 missing
TBG_measurednominal1 unique values
0 missing
TBGnominal1 unique values
0 missing
referral_sourcenominal5 unique values
0 missing

62 properties

1000000
Number of instances (rows) of the dataset.
30
Number of attributes (columns) of the dataset.
4
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.
30
Number of nominal attributes.
118.06
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.25
Second quartile (Median) of entropy among attributes.
0.47
Entropy of the target attribute values.
2.3
Average number of distinct values among the attributes of the nominal type.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
Mean skewness among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
158.27
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Mean standard deviation of attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
92.26
Percentage of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
Second quartile (Median) of skewness among attributes of the numeric type.
922578
Number of instances belonging to the most frequent class.
Minimum kurtosis among attributes of the numeric type.
66.67
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
1.6
Maximum entropy among attributes.
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
0.51
Third quartile of entropy among attributes.
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
Maximum of means among attributes of the numeric type.
1
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.02
Maximum mutual information between the nominal attributes and the target attribute.
Minimum skewness among attributes of the numeric type.
100
Percentage of nominal attributes.
0
Third quartile of mutual information between the nominal attributes and the target attribute.
5
The maximum number of distinct values among attributes of the nominal type.
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
0.09
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
0.07
Percentage of instances belonging to the least frequent class.
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.35
Average entropy of the attributes.
677
Number of instances belonging to the least frequent class.
First quartile of means among attributes of the numeric type.
0.79
Standard deviation of the number of distinct values among attributes of the nominal type.
Mean kurtosis among attributes of the numeric type.
20
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
Mean of means among attributes of the numeric type.
First quartile of skewness among attributes of the numeric type.
0
Average mutual information between the nominal attributes and the target attribute.
First quartile of standard deviation of attributes of the numeric type.
0.85
Average class difference between consecutive instances.

10 tasks

20 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Class
45 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task