Data
helena

helena

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

28 features

class (target)nominal100 unique values
0 missing
V1numeric42419 unique values
0 missing
V2numeric846 unique values
0 missing
V3numeric866 unique values
0 missing
V4numeric62001 unique values
0 missing
V5numeric63025 unique values
0 missing
V6numeric61014 unique values
0 missing
V7numeric62359 unique values
0 missing
V8numeric61798 unique values
0 missing
V9numeric60808 unique values
0 missing
V10numeric58200 unique values
0 missing
V11numeric58985 unique values
0 missing
V12numeric60800 unique values
0 missing
V13numeric62209 unique values
0 missing
V14numeric62292 unique values
0 missing
V15numeric62394 unique values
0 missing
V16numeric64062 unique values
0 missing
V17numeric64104 unique values
0 missing
V18numeric63824 unique values
0 missing
V19numeric61370 unique values
0 missing
V20numeric64274 unique values
0 missing
V21numeric63866 unique values
0 missing
V22numeric60414 unique values
0 missing
V23numeric62948 unique values
0 missing
V24numeric63014 unique values
0 missing
V25numeric64164 unique values
0 missing
V26numeric64054 unique values
0 missing
V27numeric63929 unique values
0 missing

62 properties

65196
Number of instances (rows) of the dataset.
28
Number of attributes (columns) of the dataset.
100
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.
27
Number of numeric attributes.
1
Number of nominal attributes.
111
Number of instances belonging to the least frequent class.
0.35
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.
39.47
Mean kurtosis among attributes of the numeric type.
0
First quartile of skewness among attributes of the numeric type.
20.82
Mean of means among attributes of the numeric type.
0.26
First quartile of standard deviation of attributes of the numeric type.
0.02
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
5.98
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.
0.16
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
100
Average number of distinct values among the attributes of the nominal type.
0.52
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.
-0.77
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
6.14
Percentage of instances belonging to the most frequent class.
10.71
Mean standard deviation of attributes of the numeric type.
0.39
Second quartile (Median) of skewness among attributes of the numeric type.
4005
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
3.05
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.6
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
266.33
Maximum kurtosis among attributes of the numeric type.
-0.78
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
8.78
Third quartile of kurtosis among attributes of the numeric type.
125.5
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
96.43
Percentage of numeric attributes.
33.66
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
100
The minimal number of distinct values among attributes of the nominal type.
3.57
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
100
The maximum number of distinct values among attributes of the nominal type.
-11.5
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
1.01
Third quartile of skewness among attributes of the numeric type.
2.51
Maximum skewness among attributes of the numeric type.
0.04
Minimum standard deviation of attributes of the numeric type.
-0.43
First quartile of kurtosis among attributes of the numeric type.
15.24
Third quartile of standard deviation of attributes of the numeric type.
61.38
Maximum standard deviation of attributes of the numeric type.
0.17
Percentage of instances belonging to the least frequent class.

7 tasks

4 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_class_complexity - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - 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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