Data
dilbert

dilbert

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

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V840numeric9902 unique values
0 missing
V1096numeric9881 unique values
0 missing
V1352numeric9902 unique values
0 missing
V1608numeric9864 unique values
0 missing
V1864numeric9945 unique values
0 missing
V73numeric9935 unique values
0 missing
V329numeric9935 unique values
0 missing
V585numeric9952 unique values
0 missing
V841numeric9894 unique values
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0 missing
V1353numeric9893 unique values
0 missing
V1609numeric9865 unique values
0 missing
V1865numeric9858 unique values
0 missing
V74numeric9938 unique values
0 missing
V330numeric9932 unique values
0 missing
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0 missing
V842numeric9802 unique values
0 missing
V1098numeric9872 unique values
0 missing
V1354numeric9934 unique values
0 missing
V1610numeric9874 unique values
0 missing
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0 missing
V75numeric9939 unique values
0 missing
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0 missing
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0 missing
V843numeric9801 unique values
0 missing
V1099numeric9836 unique values
0 missing
V1355numeric9930 unique values
0 missing
V1611numeric9864 unique values
0 missing
V1867numeric9933 unique values
0 missing
V76numeric9931 unique values
0 missing
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0 missing
V588numeric9960 unique values
0 missing
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0 missing
V1100numeric9853 unique values
0 missing
V1356numeric9937 unique values
0 missing
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0 missing
V77numeric9919 unique values
0 missing
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0 missing
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V845numeric9793 unique values
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0 missing
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0 missing
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0 missing
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0 missing
V1104numeric9857 unique values
0 missing
V1360numeric9912 unique values
0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
V83numeric9917 unique values
0 missing
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0 missing
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0 missing
V1107numeric9852 unique values
0 missing
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0 missing
V1619numeric9807 unique values
0 missing
V1875numeric9801 unique values
0 missing
V84numeric9920 unique values
0 missing
V340numeric9919 unique values
0 missing
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0 missing
V852numeric9788 unique values
0 missing
V1108numeric9862 unique values
0 missing
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0 missing
V1876numeric9897 unique values
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0 missing
V341numeric9917 unique values
0 missing
V597numeric9940 unique values
0 missing
V853numeric9801 unique values
0 missing
V1109numeric9893 unique values
0 missing
V1365numeric9859 unique values
0 missing
V1621numeric9822 unique values
0 missing
V1877numeric9884 unique values
0 missing
V86numeric9914 unique values
0 missing
V342numeric9920 unique values
0 missing
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0 missing
V854numeric9814 unique values
0 missing
V1110numeric9881 unique values
0 missing
V1366numeric9873 unique values
0 missing
V1622numeric9834 unique values
0 missing
V1878numeric9893 unique values
0 missing
V87numeric9907 unique values
0 missing
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0 missing
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0 missing
V855numeric9812 unique values
0 missing
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0 missing
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V88numeric9911 unique values
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V1374numeric9932 unique values
0 missing
V1630numeric9851 unique values
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0 missing
V95numeric9934 unique values
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V351numeric9947 unique values
0 missing
V607numeric9957 unique values
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V1119numeric9940 unique values
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0 missing
V1631numeric9846 unique values
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0 missing
V1120numeric9923 unique values
0 missing
V1376numeric9914 unique values
0 missing
V1632numeric9832 unique values
0 missing
V1888numeric9943 unique values
0 missing
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0 missing
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0 missing
V865numeric9800 unique values
0 missing
V1121numeric9925 unique values
0 missing
V1377numeric9906 unique values
0 missing
V1633numeric9838 unique values
0 missing
V1889numeric9937 unique values
0 missing
V98numeric9937 unique values
0 missing
V354numeric9955 unique values
0 missing
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0 missing
V866numeric9789 unique values
0 missing
V1122numeric9923 unique values
0 missing
V1378numeric9895 unique values
0 missing
V1634numeric9809 unique values
0 missing
V1890numeric9944 unique values
0 missing
V99numeric9940 unique values
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V355numeric9955 unique values
0 missing
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0 missing
V867numeric9791 unique values
0 missing
V1123numeric9922 unique values
0 missing
V1379numeric9876 unique values
0 missing
V1635numeric9824 unique values
0 missing
V1891numeric9923 unique values
0 missing
V100numeric9945 unique values
0 missing
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0 missing
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0 missing
V868numeric9785 unique values
0 missing
V1124numeric9900 unique values
0 missing
V1380numeric9883 unique values
0 missing
V1636numeric9823 unique values
0 missing
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0 missing
V101numeric9954 unique values
0 missing
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0 missing
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0 missing
V869numeric9802 unique values
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0 missing
V358numeric9948 unique values
0 missing
V614numeric9962 unique values
0 missing
V870numeric9805 unique values
0 missing
V1126numeric9880 unique values
0 missing
V1382numeric9921 unique values
0 missing
V1638numeric9837 unique values
0 missing
V1894numeric9946 unique values
0 missing
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0 missing
V615numeric9959 unique values
0 missing
V871numeric9782 unique values
0 missing
V1127numeric9856 unique values
0 missing
V1383numeric9925 unique values
0 missing
V1639numeric9835 unique values
0 missing
V1895numeric9940 unique values
0 missing
V104numeric9953 unique values
0 missing
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0 missing
V616numeric9963 unique values
0 missing
V872numeric9819 unique values
0 missing
V1128numeric9846 unique values
0 missing
V1384numeric9946 unique values
0 missing
V1640numeric9844 unique values
0 missing
V1896numeric9963 unique values
0 missing
V105numeric9944 unique values
0 missing
V361numeric9924 unique values
0 missing
V617numeric9965 unique values
0 missing
V873numeric9822 unique values
0 missing
V1129numeric9841 unique values
0 missing
V1385numeric9947 unique values
0 missing
V1641numeric9861 unique values
0 missing
V1897numeric9942 unique values
0 missing
V106numeric9935 unique values
0 missing
V362numeric9904 unique values
0 missing
V618numeric9963 unique values
0 missing
V874numeric9826 unique values
0 missing
V1130numeric9844 unique values
0 missing
V1386numeric9933 unique values
0 missing
V1642numeric9850 unique values
0 missing
V1898numeric9959 unique values
0 missing
V107numeric9929 unique values
0 missing
V363numeric9916 unique values
0 missing
V619numeric9949 unique values
0 missing
V875numeric9831 unique values
0 missing
V1131numeric9833 unique values
0 missing
V1387numeric9933 unique values
0 missing
V1643numeric9842 unique values
0 missing
V1899numeric9919 unique values
0 missing
V108numeric9940 unique values
0 missing
V364numeric9917 unique values
0 missing
V620numeric9942 unique values
0 missing
V876numeric9825 unique values
0 missing
V1132numeric9836 unique values
0 missing
V1388numeric9918 unique values
0 missing
V1644numeric9833 unique values
0 missing
V1900numeric9919 unique values
0 missing
V109numeric9915 unique values
0 missing
V365numeric9922 unique values
0 missing
V621numeric9950 unique values
0 missing
V877numeric9817 unique values
0 missing
V1133numeric9825 unique values
0 missing
V1389numeric9892 unique values
0 missing
V1645numeric9824 unique values
0 missing
V1901numeric9906 unique values
0 missing
V110numeric9927 unique values
0 missing
V366numeric9913 unique values
0 missing
V622numeric9943 unique values
0 missing
V878numeric9826 unique values
0 missing
V1134numeric9850 unique values
0 missing
V1390numeric9850 unique values
0 missing
V1646numeric9822 unique values
0 missing
V1902numeric9943 unique values
0 missing
V111numeric9917 unique values
0 missing
V367numeric9898 unique values
0 missing
V623numeric9933 unique values
0 missing
V879numeric9834 unique values
0 missing
V1135numeric9856 unique values
0 missing
V1391numeric9811 unique values
0 missing
V1647numeric9826 unique values
0 missing
V1903numeric9826 unique values
0 missing
V112numeric9935 unique values
0 missing
V368numeric9908 unique values
0 missing
V624numeric9951 unique values
0 missing
V880numeric9827 unique values
0 missing
V1136numeric9876 unique values
0 missing
V1392numeric9819 unique values
0 missing
V1648numeric9810 unique values
0 missing
V1904numeric9969 unique values
0 missing
V113numeric9924 unique values
0 missing
V369numeric9926 unique values
0 missing
V625numeric9943 unique values
0 missing
V881numeric9857 unique values
0 missing
V1137numeric9880 unique values
0 missing
V1393numeric9819 unique values
0 missing
V1649numeric9843 unique values
0 missing
V1905numeric9945 unique values
0 missing
V114numeric9932 unique values
0 missing
V370numeric9919 unique values
0 missing
V626numeric9962 unique values
0 missing
V882numeric9867 unique values
0 missing
V1138numeric9879 unique values
0 missing
V1394numeric9842 unique values
0 missing
V1650numeric9853 unique values
0 missing
V1906numeric9932 unique values
0 missing
V115numeric9928 unique values
0 missing
V371numeric9938 unique values
0 missing
V627numeric9941 unique values
0 missing
V883numeric9849 unique values
0 missing
V1139numeric9913 unique values
0 missing
V1395numeric9870 unique values
0 missing
V1651numeric9804 unique values
0 missing
V1907numeric9956 unique values
0 missing
V116numeric9902 unique values
0 missing
V372numeric9954 unique values
0 missing
V628numeric9936 unique values
0 missing
V884numeric9836 unique values
0 missing
V1140numeric9885 unique values
0 missing
V1396numeric9901 unique values
0 missing
V1652numeric9856 unique values
0 missing
V1908numeric9864 unique values
0 missing
V117numeric9910 unique values
0 missing
V373numeric9948 unique values
0 missing
V629numeric9930 unique values
0 missing
V885numeric9798 unique values
0 missing
V1141numeric9908 unique values
0 missing
V1397numeric9884 unique values
0 missing
V1653numeric9806 unique values
0 missing
V1909numeric9960 unique values
0 missing
V118numeric9920 unique values
0 missing
V374numeric9943 unique values
0 missing
V630numeric9947 unique values
0 missing
V886numeric9822 unique values
0 missing
V1142numeric9913 unique values
0 missing
V1398numeric9918 unique values
0 missing
V1654numeric9790 unique values
0 missing
V1910numeric9856 unique values
0 missing
V119numeric9916 unique values
0 missing
V375numeric9930 unique values
0 missing
V631numeric9946 unique values
0 missing
V887numeric9812 unique values
0 missing
V1143numeric9909 unique values
0 missing
V1399numeric9926 unique values
0 missing
V1655numeric9804 unique values
0 missing
V1911numeric9888 unique values
0 missing
V120numeric9928 unique values
0 missing
V376numeric9930 unique values
0 missing
V632numeric9956 unique values
0 missing
V888numeric9828 unique values
0 missing
V1144numeric9937 unique values
0 missing
V1400numeric9935 unique values
0 missing
V1656numeric9796 unique values
0 missing
V1912numeric9943 unique values
0 missing
V121numeric9924 unique values
0 missing
V377numeric9923 unique values
0 missing
V633numeric9955 unique values
0 missing
V889numeric9822 unique values
0 missing
V1145numeric9938 unique values
0 missing
V1401numeric9929 unique values
0 missing
V1657numeric9805 unique values
0 missing
V1913numeric9927 unique values
0 missing
V122numeric9929 unique values
0 missing
V378numeric9919 unique values
0 missing
V634numeric9951 unique values
0 missing
V890numeric9813 unique values
0 missing
V1146numeric9932 unique values
0 missing
V1402numeric9944 unique values
0 missing
V1658numeric9768 unique values
0 missing
V1914numeric9962 unique values
0 missing
V123numeric9914 unique values
0 missing
V379numeric9940 unique values
0 missing
V635numeric9961 unique values
0 missing
V891numeric9832 unique values
0 missing
V1147numeric9943 unique values
0 missing
V1403numeric9935 unique values
0 missing
V1659numeric9797 unique values
0 missing
V1915numeric9946 unique values
0 missing
V124numeric9930 unique values
0 missing
V380numeric9949 unique values
0 missing
V636numeric9945 unique values
0 missing
V892numeric9845 unique values
0 missing
V1148numeric9943 unique values
0 missing
V1404numeric9930 unique values
0 missing
V1660numeric9812 unique values
0 missing
V1916numeric9954 unique values
0 missing

62 properties

10000
Number of instances (rows) of the dataset.
2001
Number of attributes (columns) of the dataset.
5
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.
2000
Number of numeric attributes.
1
Number of nominal attributes.
0.2
Number of attributes divided by the number of instances.
5
Average number of distinct values among the attributes of the nominal type.
2.65
Second quartile (Median) of kurtosis 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.
1.66
Mean skewness among attributes of the numeric type.
0.2
Second quartile (Median) of means among attributes of the numeric type.
20.49
Percentage of instances belonging to the most frequent class.
0.17
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
2049
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.5
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-0.84
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.11
Second quartile (Median) of standard deviation of attributes of the numeric type.
74.45
Maximum kurtosis among attributes of the numeric type.
0.03
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
1.09
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
7.36
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
5
The minimal number of distinct values among attributes of the nominal type.
99.95
Percentage of numeric attributes.
0.3
Third quartile of means among attributes of the numeric type.
5
The maximum number of distinct values among attributes of the nominal type.
0.26
Minimum skewness among attributes of the numeric type.
0.05
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
6.18
Maximum skewness among attributes of the numeric type.
0.01
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
2.31
Third quartile of skewness among attributes of the numeric type.
0.62
Maximum standard deviation of attributes of the numeric type.
19.13
Percentage of instances belonging to the least frequent class.
0.67
First quartile of kurtosis among attributes of the numeric type.
0.25
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
1913
Number of instances belonging to the least frequent class.
0.1
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.
5.52
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.24
Mean of means among attributes of the numeric type.
0.73
First quartile of skewness among attributes of the numeric type.
0.2
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.07
First quartile of standard deviation of attributes of the numeric type.
2.32
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.
Second quartile (Median) of entropy among attributes.

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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