Data
robert

robert

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

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0 missing
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0 missing
V6174numeric712 unique values
0 missing
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0 missing
V6686numeric707 unique values
0 missing
V6942numeric751 unique values
0 missing
V7198numeric790 unique values
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V287numeric798 unique values
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V6687numeric256 unique values
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V32numeric735 unique values
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V2336numeric747 unique values
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V2592numeric707 unique values
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V3104numeric650 unique values
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V3360numeric621 unique values
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V3872numeric605 unique values
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V4641numeric740 unique values
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V4897numeric781 unique values
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V5665numeric645 unique values
0 missing
V5921numeric799 unique values
0 missing
V6177numeric789 unique values
0 missing
V6433numeric787 unique values
0 missing
V6689numeric256 unique values
0 missing
V6945numeric752 unique values
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V34numeric734 unique values
0 missing
V290numeric806 unique values
0 missing
V546numeric753 unique values
0 missing
V802numeric771 unique values
0 missing
V1058numeric256 unique values
0 missing
V1314numeric791 unique values
0 missing
V1570numeric736 unique values
0 missing
V1826numeric805 unique values
0 missing
V2082numeric785 unique values
0 missing
V2338numeric735 unique values
0 missing
V2594numeric816 unique values
0 missing
V2850numeric256 unique values
0 missing
V3106numeric668 unique values
0 missing
V3362numeric824 unique values
0 missing
V3618numeric804 unique values
0 missing

62 properties

10000
Number of instances (rows) of the dataset.
7201
Number of attributes (columns) of the dataset.
10
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.
7200
Number of numeric attributes.
1
Number of nominal attributes.
89.34
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.88
Third quartile of skewness among attributes of the numeric type.
2.22
Maximum skewness among attributes of the numeric type.
9.58
Percentage of instances belonging to the least frequent class.
-0.77
First quartile of kurtosis among attributes of the numeric type.
216.8
Third quartile of standard deviation of attributes of the numeric type.
275.82
Maximum standard deviation of attributes of the numeric type.
958
Number of instances belonging to the least frequent class.
313.27
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.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
-0.31
Mean kurtosis among attributes of the numeric type.
-0.14
First quartile of skewness among attributes of the numeric type.
413.46
Mean of means among attributes of the numeric type.
161.5
First quartile of standard deviation of attributes of the numeric type.
0.1
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
3.32
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.
-0.5
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.72
Number of attributes divided by the number of instances.
10
Average number of distinct values among the attributes of the nominal type.
441.18
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.
0.31
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
10.43
Percentage of instances belonging to the most frequent class.
191.15
Mean standard deviation of attributes of the numeric type.
0.17
Second quartile (Median) of skewness among attributes of the numeric type.
1043
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
195.28
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.34
Minimum kurtosis among attributes of the numeric type.
207.72
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
4.15
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
0.08
Third quartile of kurtosis among attributes of the numeric type.
716.46
Maximum of means among attributes of the numeric type.
10
The minimal number of distinct values among attributes of the nominal type.
99.99
Percentage of numeric attributes.
485.56
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
-1.43
Minimum skewness among attributes of the numeric type.
0.01
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
10
The maximum number of distinct values among attributes of the nominal type.

7 tasks

5 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_class_complexity - 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 - 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
Define a new task