Data
fabert

fabert

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

801 features

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0 missing
V462numeric43 unique values
0 missing
V718numeric20 unique values
0 missing
V207numeric70 unique values
0 missing
V463numeric22 unique values
0 missing
V719numeric11 unique values
0 missing
V208numeric22 unique values
0 missing
V464numeric33 unique values
0 missing
V720numeric14 unique values
0 missing
V209numeric11 unique values
0 missing
V465numeric79 unique values
0 missing
V721numeric47 unique values
0 missing
V210numeric55 unique values
0 missing
V466numeric61 unique values
0 missing
V722numeric13 unique values
0 missing
V211numeric13 unique values
0 missing
V467numeric7 unique values
0 missing
V723numeric61 unique values
0 missing
V212numeric31 unique values
0 missing
V468numeric61 unique values
0 missing
V724numeric28 unique values
0 missing
V213numeric9 unique values
0 missing
V469numeric29 unique values
0 missing
V725numeric63 unique values
0 missing
V214numeric16 unique values
0 missing
V470numeric19 unique values
0 missing
V726numeric137 unique values
0 missing
V215numeric2 unique values
0 missing
V471numeric17 unique values
0 missing
V727numeric15 unique values
0 missing
V216numeric27 unique values
0 missing
V472numeric36 unique values
0 missing
V728numeric10 unique values
0 missing
V217numeric2 unique values
0 missing
V473numeric64 unique values
0 missing
V729numeric61 unique values
0 missing
V218numeric36 unique values
0 missing
V474numeric96 unique values
0 missing
V730numeric91 unique values
0 missing
V219numeric120 unique values
0 missing
V475numeric38 unique values
0 missing
V731numeric17 unique values
0 missing
V220numeric16 unique values
0 missing
V476numeric22 unique values
0 missing
V732numeric18 unique values
0 missing
V221numeric15 unique values
0 missing
V477numeric31 unique values
0 missing
V733numeric59 unique values
0 missing
V222numeric10 unique values
0 missing
V478numeric72 unique values
0 missing
V734numeric103 unique values
0 missing
V223numeric75 unique values
0 missing
V479numeric74 unique values
0 missing
V735numeric18 unique values
0 missing
V224numeric19 unique values
0 missing
V480numeric101 unique values
0 missing
V736numeric41 unique values
0 missing
V225numeric100 unique values
0 missing
V481numeric41 unique values
0 missing
V737numeric4 unique values
0 missing
V226numeric94 unique values
0 missing
V482numeric29 unique values
0 missing
V738numeric41 unique values
0 missing
V227numeric37 unique values
0 missing
V483numeric30 unique values
0 missing
V739numeric7 unique values
0 missing
V228numeric51 unique values
0 missing
V484numeric24 unique values
0 missing
V740numeric7 unique values
0 missing
V229numeric130 unique values
0 missing
V485numeric57 unique values
0 missing
V741numeric44 unique values
0 missing
V230numeric93 unique values
0 missing
V486numeric37 unique values
0 missing
V742numeric72 unique values
0 missing
V231numeric158 unique values
0 missing
V487numeric76 unique values
0 missing
V743numeric107 unique values
0 missing
V232numeric23 unique values
0 missing
V488numeric102 unique values
0 missing
V744numeric64 unique values
0 missing
V233numeric70 unique values
0 missing
V489numeric44 unique values
0 missing
V745numeric73 unique values
0 missing
V234numeric28 unique values
0 missing
V490numeric99 unique values
0 missing
V746numeric36 unique values
0 missing
V235numeric6 unique values
0 missing
V491numeric88 unique values
0 missing
V747numeric62 unique values
0 missing
V236numeric13 unique values
0 missing
V492numeric56 unique values
0 missing
V748numeric125 unique values
0 missing
V237numeric70 unique values
0 missing
V493numeric87 unique values
0 missing
V749numeric57 unique values
0 missing
V238numeric4 unique values
0 missing
V494numeric29 unique values
0 missing
V750numeric78 unique values
0 missing
V239numeric78 unique values
0 missing
V495numeric27 unique values
0 missing
V751numeric2 unique values
0 missing
V240numeric16 unique values
0 missing
V496numeric67 unique values
0 missing
V752numeric109 unique values
0 missing
V241numeric89 unique values
0 missing
V497numeric43 unique values
0 missing
V753numeric49 unique values
0 missing
V242numeric1 unique values
0 missing
V498numeric76 unique values
0 missing
V754numeric13 unique values
0 missing
V243numeric21 unique values
0 missing
V499numeric14 unique values
0 missing
V755numeric2 unique values
0 missing
V244numeric36 unique values
0 missing
V500numeric16 unique values
0 missing
V756numeric120 unique values
0 missing
V245numeric80 unique values
0 missing
V501numeric30 unique values
0 missing
V757numeric40 unique values
0 missing
V246numeric80 unique values
0 missing
V502numeric122 unique values
0 missing
V758numeric35 unique values
0 missing
V247numeric21 unique values
0 missing
V503numeric35 unique values
0 missing
V759numeric5 unique values
0 missing
V248numeric21 unique values
0 missing
V504numeric99 unique values
0 missing
V760numeric26 unique values
0 missing
V249numeric19 unique values
0 missing
V505numeric87 unique values
0 missing
V761numeric59 unique values
0 missing
V250numeric59 unique values
0 missing
V506numeric17 unique values
0 missing
V762numeric20 unique values
0 missing
V251numeric87 unique values
0 missing
V507numeric57 unique values
0 missing
V763numeric83 unique values
0 missing
V252numeric9 unique values
0 missing
V508numeric29 unique values
0 missing
V764numeric56 unique values
0 missing
V253numeric2 unique values
0 missing
V509numeric80 unique values
0 missing
V765numeric27 unique values
0 missing
V254numeric37 unique values
0 missing
V510numeric91 unique values
0 missing
V766numeric15 unique values
0 missing
V255numeric97 unique values
0 missing
V511numeric90 unique values
0 missing
V767numeric66 unique values
0 missing

62 properties

8237
Number of instances (rows) of the dataset.
801
Number of attributes (columns) of the dataset.
7
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.
800
Number of numeric attributes.
1
Number of nominal attributes.
12
First quartile of skewness among attributes of the numeric type.
0.01
Mean of means among attributes of the numeric type.
0.04
First quartile of standard deviation of attributes of the numeric type.
0.17
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
2.7
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.
224.4
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.1
Number of attributes divided by the number of instances.
7
Average number of distinct values among the attributes of the nominal type.
0
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.
16.98
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
23.39
Percentage of instances belonging to the most frequent class.
0.06
Mean standard deviation of attributes of the numeric type.
14.37
Second quartile (Median) of skewness among attributes of the numeric type.
1927
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
0.06
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
45.63
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
8237
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
360.4
Third quartile of kurtosis among attributes of the numeric type.
0.02
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
99.88
Percentage of numeric attributes.
0.01
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
7
The minimal number of distinct values among attributes of the nominal type.
0.12
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
7
The maximum number of distinct values among attributes of the nominal type.
6.22
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
18.29
Third quartile of skewness among attributes of the numeric type.
90.76
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
154.78
First quartile of kurtosis among attributes of the numeric type.
0.07
Third quartile of standard deviation of attributes of the numeric type.
0.11
Maximum standard deviation of attributes of the numeric type.
6.09
Percentage of instances belonging to the least frequent class.
502
Number of instances belonging to the least frequent class.
0
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.
415.56
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
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