Data
numerai28.6

numerai28.6

active ARFF Publicly available Visibility: public Uploaded 07-07-2016 by Joaquin Vanschoren
2 likes downloaded by 5 people , 8 total downloads 1 issues 1 downvotes
  • AzurePilot Computational Universe Machine Learning OpenML-CC18 study_135 study_218 study_99 study_271 study_240 study_446 study_447 study_448 study_449 study_226 study_275
Issue #Downvotes for this reason By
1User 4095


Loading wiki
Help us complete this description Edit
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

107 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.
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.5
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
50.52
Percentage of instances belonging to the most frequent class.
0.29
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.5
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
48658
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-1.19
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.3
Minimum kurtosis among attributes of the numeric type.
0.5
Second quartile (Median) of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.49
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
-1.13
Maximum kurtosis among attributes of the numeric type.
0.49
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.5
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.52
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.01
Second quartile (Median) of skewness among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
4.55
Percentage of binary attributes.
0.29
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.5
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
-0.1
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.07
Maximum skewness among attributes of the numeric type.
0.28
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
-1.17
Third quartile of kurtosis among attributes of the numeric type.
0.5
Average class difference between consecutive instances.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.48
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.3
Maximum standard deviation of attributes of the numeric type.
49.48
Percentage of instances belonging to the least frequent class.
95.45
Percentage of numeric attributes.
0.51
Third quartile of means among attributes of the numeric type.
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.5
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
47662
Number of instances belonging to the least frequent class.
4.55
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.48
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-1.2
Mean kurtosis among attributes of the numeric type.
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.02
Third quartile of skewness among attributes of the numeric type.
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.48
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.5
Mean of means among attributes of the numeric type.
0.48
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-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.
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.5
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.49
First quartile of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.48
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.5
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.48
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
-0.03
First quartile of skewness among attributes of the numeric type.
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
-0
Mean skewness among attributes of the numeric type.
0.29
First quartile of standard deviation of attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.48
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

17 tasks

3035 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: attribute_21
4 runs - estimation_procedure: 33% Holdout set - target_feature: attribute_21
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: attribute_21
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: attribute_21
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: attribute_21
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: classification problem
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
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