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numerai28.6

numerai28.6

active ARFF Publicly available Visibility: public Uploaded 07-07-2016 by Joaquin Vanschoren
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  • AzurePilot OpenML-CC18 study_135 study_218 study_99
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Author: Numer.ai Source: [Kaggle](https://www.kaggle.com/numerai/encrypted-stock-market-data-from-numerai) Please cite: Encrypted Stock Market Training Data from Numer.ai The data is cleaned, regularized and encrypted global equity data. The first 21 columns (feature1 - feature21) are features, and target is the binary class you’re trying to predict.

22 features

attribute_21 (target)nominal2 unique values
0 missing
attribute_0numeric1001 unique values
0 missing
attribute_1numeric1001 unique values
0 missing
attribute_2numeric1000 unique values
0 missing
attribute_3numeric1001 unique values
0 missing
attribute_4numeric1000 unique values
0 missing
attribute_5numeric1001 unique values
0 missing
attribute_6numeric1000 unique values
0 missing
attribute_7numeric1001 unique values
0 missing
attribute_8numeric1001 unique values
0 missing
attribute_9numeric1000 unique values
0 missing
attribute_10numeric1001 unique values
0 missing
attribute_11numeric1001 unique values
0 missing
attribute_12numeric1000 unique values
0 missing
attribute_13numeric1001 unique values
0 missing
attribute_14numeric1001 unique values
0 missing
attribute_15numeric1000 unique values
0 missing
attribute_16numeric1001 unique values
0 missing
attribute_17numeric1001 unique values
0 missing
attribute_18numeric1001 unique values
0 missing
attribute_19numeric1001 unique values
0 missing
attribute_20numeric1001 unique values
0 missing

62 properties

96320
Number of instances (rows) of the dataset.
22
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.
21
Number of numeric attributes.
1
Number of nominal attributes.
Second quartile (Median) of entropy among attributes.
1
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.
-1.19
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
-0
Mean skewness among attributes of the numeric type.
0.5
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.
0.29
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
50.52
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.01
Second quartile (Median) of skewness among attributes of the numeric type.
48658
Number of instances belonging to the most frequent class.
-1.3
Minimum kurtosis among attributes of the numeric type.
4.55
Percentage of binary attributes.
0.29
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
0.49
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
-1.13
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
-1.17
Third quartile of kurtosis among attributes of the numeric type.
0.52
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
95.45
Percentage of numeric attributes.
0.51
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
-0.1
Minimum skewness among attributes of the numeric type.
4.55
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
0.28
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.02
Third quartile of skewness among attributes of the numeric type.
0.07
Maximum skewness among attributes of the numeric type.
49.48
Percentage of instances belonging to the least frequent class.
-1.23
First quartile of kurtosis among attributes of the numeric type.
0.29
Third quartile of standard deviation of attributes of the numeric type.
0.3
Maximum standard deviation of attributes of the numeric type.
47662
Number of instances belonging to the least frequent class.
0.49
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
-1.2
Mean kurtosis among attributes of the numeric type.
-0.03
First quartile of skewness among attributes of the numeric type.
0.5
Mean of means among attributes of the numeric type.
0.29
First quartile of standard deviation of attributes of the numeric type.
0.5
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.

6 tasks

852 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: attribute_21
0 runs - estimation_procedure: 33% Holdout set - target_feature: attribute_21
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: attribute_21
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task