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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

23 features

class (target)nominal2 unique values
0 missing
V11nominal28 unique values
0 missing
V22nominal20 unique values
0 missing
V21numeric431 unique values
1500 missing
V20numeric406 unique values
40 missing
V19nominal3 unique values
0 missing
V18nominal3 unique values
0 missing
V17nominal4 unique values
0 missing
V16nominal1097 unique values
0 missing
V15nominal1 unique values
0 missing
V14nominal3 unique values
0 missing
V13nominal24 unique values
0 missing
V12nominal1199 unique values
0 missing
V10nominal1609 unique values
0 missing
V9nominal857 unique values
0 missing
V8nominal3 unique values
0 missing
V7nominal2580 unique values
0 missing
V6numeric57 unique values
0 missing
V5numeric71 unique values
0 missing
V4numeric160 unique values
16279 missing
V3numeric1140 unique values
10182 missing
V2numeric9 unique values
1715 missing
V1numeric580 unique values
40 missing
Show all 23 features

62 properties

NumberOfInstances
31406
Number of instances (rows) of the dataset.
NumberOfFeatures
23
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
29756
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
17204
Number of instances with at least one value missing.
NumberOfNumericFeatures
8
Number of numeric attributes.
NumberOfSymbolicFeatures
15
Number of nominal attributes.
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
4.35
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
54.78
Percentage of instances having missing values.
PercentageOfMissingValues
4.12
Percentage of missing values.
PercentageOfNumericFeatures
34.78
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
65.22
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.96
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.8
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
21.56
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
799.07
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
0.56
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
9.73
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
2.11
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
3.75
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
149.35
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.02
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
1.85
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
83.98
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
7.25
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
13.29
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
226.19
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.03
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
2.71
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
143.33
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
1
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
347.84
Mean of means among attributes of the numeric type.
ClassEntropy
0.45
Entropy of the target attribute values.
Dimensionality
0
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
13.05
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MajorityClassPercentage
90.46
Percentage of instances belonging to the most frequent class.
MajorityClassSize
28411
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
9.72
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
20.62
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
1978.97
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.28
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2580
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
3.49
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
448.05
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
3.73
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
6.25
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
1
Number of binary attributes.
MeanMutualInformation
0.03
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
106.28
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
495.53
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
1.7
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
115.78
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
-0
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.45
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
7
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
1
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.48
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
6.18
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
9.54
Percentage of instances belonging to the least frequent class.
MinorityClassSize
2995
Number of instances belonging to the least frequent class.
Show all 62 properties

16 tasks

Supervised Classification on rl
1 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on rl
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on rl
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on rl
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on rl
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on rl
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on rl
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on rl
0 runs - estimation_procedure: 50 times Clustering
Clustering on rl
0 runs - estimation_procedure: 50 times Clustering
Clustering on rl
0 runs - estimation_procedure: 50 times Clustering
Clustering on rl
0 runs - estimation_procedure: 50 times Clustering
Clustering on rl
0 runs - estimation_procedure: 50 times Clustering
Clustering on rl
0 runs - estimation_procedure: 50 times Clustering
Clustering on rl
0 runs - estimation_procedure: 50 times Clustering
Clustering on rl
0 runs - estimation_procedure: 50 times Clustering
Clustering on rl
0 runs - estimation_procedure: 50 times Clustering
Define a new task

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