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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
Show first 100 features

62 properties

NumberOfInstances
3153
Number of instances (rows) of the dataset.
NumberOfFeatures
971
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
970
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
0.1
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
99.9
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0.1
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-1.52
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
11.24
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
0.54
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
46.14
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.84
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
46.75
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
1.6
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
88.06
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
18.21
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
92.7
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
4.37
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
108.74
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.5
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
53.73
Mean of means among attributes of the numeric type.
ClassEntropy
1
Entropy of the target attribute values.
Dimensionality
0.31
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MajorityClassPercentage
50.84
Percentage of instances belonging to the most frequent class.
MajorityClassSize
1603
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
272.04
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
143.42
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
15.72
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
113.89
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
17.9
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
1
Number of binary attributes.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
2.92
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
77.08
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.83
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
1.07
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.3
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
12.9
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
49.16
Percentage of instances belonging to the least frequent class.
MinorityClassSize
1550
Number of instances belonging to the least frequent class.
Show all 62 properties

19 tasks

Supervised Classification on gina
0 runs - estimation_procedure: Test on Training Data - target_feature: class
Supervised Classification on gina
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on gina
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on gina
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
Supervised Classification on gina
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
Learning Curve on gina
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on gina
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on gina
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on gina
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on gina
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on gina
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina
0 runs - estimation_procedure: 50 times Clustering
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