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active ARFF Publicly available Visibility: public Uploaded 16-08-2018 by Janek Thomas
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The goal of this challenge is to expose the research community to real world datasets of interest to 4Paradigm. All datasets are formatted in a uniform way, though the type of data might differ. The data are provided as preprocessed matrices, so that participants can focus on classification, although participants are welcome to use additional feature extraction procedures (as long as they do not violate any rule of the challenge). All problems are binary classification problems and are assessed with the normalized Area Under the ROC Curve (AUC) metric (i.e. 2*AUC-1). The identity of the datasets and the type of data is concealed, though its structure is revealed. The final score in phase 2 will be the average of rankings on all testing datasets, a ranking will be generated from such results, and winners will be determined according to such ranking. The tasks are constrained by a time budget. The Codalab platform provides computational resources shared by all participants. Each code submission will be exceuted in a compute worker with the following characteristics: 2Cores / 8G Memory / 40G SSD with Ubuntu OS. To ensure the fairness of the evaluation, when a code submission is evaluated, its execution time is limited in time. http://automl.chalearn.org/data

971 features

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0 missing
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0 missing
V213numeric250 unique values
0 missing
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0 missing
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0 missing
V729numeric136 unique values
0 missing
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0 missing
V474numeric212 unique values
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0 missing
V219numeric249 unique values
0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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V743numeric248 unique values
0 missing
V232numeric229 unique values
0 missing
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V744numeric207 unique values
0 missing
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V745numeric253 unique values
0 missing
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V490numeric194 unique values
0 missing
V746numeric240 unique values
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V747numeric97 unique values
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0 missing
V748numeric190 unique values
0 missing
V237numeric246 unique values
0 missing
V493numeric220 unique values
0 missing
V749numeric247 unique values
0 missing
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V239numeric147 unique values
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0 missing
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0 missing
V753numeric79 unique values
0 missing
V242numeric250 unique values
0 missing
V498numeric246 unique values
0 missing
V754numeric200 unique values
0 missing
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0 missing
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V755numeric207 unique values
0 missing
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0 missing
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0 missing
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0 missing
V759numeric39 unique values
0 missing
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0 missing
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0 missing
V760numeric248 unique values
0 missing
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0 missing
V505numeric130 unique values
0 missing
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0 missing
V250numeric251 unique values
0 missing
V506numeric167 unique values
0 missing
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0 missing
V251numeric243 unique values
0 missing
V507numeric229 unique values
0 missing
V763numeric221 unique values
0 missing
V252numeric249 unique values
0 missing
V508numeric111 unique values
0 missing
V764numeric138 unique values
0 missing
V253numeric90 unique values
0 missing
V509numeric246 unique values
0 missing
V765numeric252 unique values
0 missing
V254numeric251 unique values
0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing

62 properties

3153
Number of instances (rows) of the dataset.
971
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.
970
Number of numeric attributes.
1
Number of nominal attributes.
0.54
First quartile of skewness among attributes of the numeric type.
53.73
Mean of means among attributes of the numeric type.
46.14
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.
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.
0.84
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.31
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
46.75
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.
2.92
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
50.84
Percentage of instances belonging to the most frequent class.
77.08
Mean standard deviation of attributes of the numeric type.
1.6
Second quartile (Median) of skewness among attributes of the numeric type.
1603
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.1
Percentage of binary attributes.
88.06
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.83
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
272.04
Maximum kurtosis among attributes of the numeric type.
1.07
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
18.21
Third quartile of kurtosis among attributes of the numeric type.
143.42
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
99.9
Percentage of numeric attributes.
92.7
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
0.1
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.
-0.3
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
4.37
Third quartile of skewness among attributes of the numeric type.
15.72
Maximum skewness among attributes of the numeric type.
12.9
Minimum standard deviation of attributes of the numeric type.
-1.52
First quartile of kurtosis among attributes of the numeric type.
108.74
Third quartile of standard deviation of attributes of the numeric type.
113.89
Maximum standard deviation of attributes of the numeric type.
49.16
Percentage of instances belonging to the least frequent class.
1550
Number of instances belonging to the least frequent class.
11.24
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.
17.9
Mean kurtosis among attributes of the numeric type.

8 tasks

0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Test on Training Data - 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
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