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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

49 features

class (target)nominal2 unique values
0 missing
V1numeric2 unique values
0 missing
V2numeric2 unique values
0 missing
V3numeric2 unique values
0 missing
V4numeric330 unique values
0 missing
V5numeric2 unique values
0 missing
V6numeric2 unique values
0 missing
V7numeric2 unique values
0 missing
V8numeric2 unique values
0 missing
V9numeric2 unique values
0 missing
V10numeric48 unique values
0 missing
V11numeric2 unique values
0 missing
V12numeric2 unique values
0 missing
V13numeric2 unique values
0 missing
V14numeric1 unique values
0 missing
V15numeric16 unique values
0 missing
V16numeric2 unique values
0 missing
V17numeric2 unique values
0 missing
V18numeric2 unique values
0 missing
V19numeric2 unique values
0 missing
V20numeric2 unique values
0 missing
V21numeric1 unique values
0 missing
V22numeric2 unique values
0 missing
V23numeric2 unique values
0 missing
V24numeric2 unique values
0 missing
V25numeric69 unique values
0 missing
V26numeric2 unique values
0 missing
V27numeric2 unique values
0 missing
V28numeric2 unique values
0 missing
V29numeric2 unique values
0 missing
V30numeric2 unique values
0 missing
V31numeric2 unique values
0 missing
V32numeric51 unique values
0 missing
V33numeric2 unique values
0 missing
V34numeric2 unique values
0 missing
V35numeric2 unique values
0 missing
V36numeric2 unique values
0 missing
V37numeric2 unique values
0 missing
V38numeric2 unique values
0 missing
V39numeric2 unique values
0 missing
V40numeric75 unique values
0 missing
V41numeric2 unique values
0 missing
V42numeric2 unique values
0 missing
V43numeric2 unique values
0 missing
V44numeric2 unique values
0 missing
V45numeric2 unique values
0 missing
V46numeric2 unique values
0 missing
V47numeric2 unique values
0 missing
V48numeric2 unique values
0 missing

62 properties

4147
Number of instances (rows) of the dataset.
49
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.
48
Number of numeric attributes.
1
Number of nominal attributes.
1.97
First quartile of skewness among attributes of the numeric type.
34.09
Mean of means among attributes of the numeric type.
0.17
First quartile of standard deviation of attributes of the numeric type.
0.62
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
0.81
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.
9.71
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.01
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
0.08
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.
5.94
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
75.19
Percentage of instances belonging to the most frequent class.
14.08
Mean standard deviation of attributes of the numeric type.
3.31
Second quartile (Median) of skewness among attributes of the numeric type.
3118
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
2.04
Percentage of binary attributes.
0.27
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.98
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
4147
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
28.79
Third quartile of kurtosis among attributes of the numeric type.
631.89
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
97.96
Percentage of numeric attributes.
0.31
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.
2.04
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.
-2.08
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
5.55
Third quartile of skewness among attributes of the numeric type.
64.4
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
2.31
First quartile of kurtosis among attributes of the numeric type.
0.42
Third quartile of standard deviation of attributes of the numeric type.
161.41
Maximum standard deviation of attributes of the numeric type.
24.81
Percentage of instances belonging to the least frequent class.
1029
Number of instances belonging to the least frequent class.
0.03
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.
158.06
Mean kurtosis among attributes of the numeric type.

7 tasks

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 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