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volkert

volkert

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

181 features

class (target)nominal10 unique values
0 missing
V1numeric3441 unique values
0 missing
V2numeric1 unique values
0 missing
V3numeric1 unique values
0 missing
V4numeric1 unique values
0 missing
V5numeric1 unique values
0 missing
V6numeric1 unique values
0 missing
V7numeric1 unique values
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V8numeric1 unique values
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V9numeric1 unique values
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V10numeric1406 unique values
0 missing
V11numeric1 unique values
0 missing
V12numeric1 unique values
0 missing
V13numeric1 unique values
0 missing
V14numeric1 unique values
0 missing
V15numeric1 unique values
0 missing
V16numeric1 unique values
0 missing
V17numeric1 unique values
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V18numeric3445 unique values
0 missing
V19numeric1 unique values
0 missing
V20numeric1 unique values
0 missing
V21numeric1 unique values
0 missing
V22numeric1 unique values
0 missing
V23numeric1 unique values
0 missing
V24numeric1 unique values
0 missing
V25numeric1 unique values
0 missing
V26numeric1 unique values
0 missing
V27numeric1 unique values
0 missing
V28numeric1 unique values
0 missing
V29numeric1 unique values
0 missing
V30numeric1 unique values
0 missing
V31numeric1 unique values
0 missing
V32numeric1 unique values
0 missing
V33numeric1 unique values
0 missing
V34numeric1 unique values
0 missing
V35numeric1 unique values
0 missing
V36numeric1 unique values
0 missing
V37numeric3255 unique values
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V38numeric3322 unique values
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V39numeric3016 unique values
0 missing
V40numeric2692 unique values
0 missing
V41numeric2527 unique values
0 missing
V42numeric2308 unique values
0 missing
V43numeric2201 unique values
0 missing
V44numeric1873 unique values
0 missing
V45numeric1771 unique values
0 missing
V46numeric1621 unique values
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V47numeric1535 unique values
0 missing
V48numeric1440 unique values
0 missing
V49numeric1382 unique values
0 missing
V50numeric1261 unique values
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V51numeric1134 unique values
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V52numeric981 unique values
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V53numeric969 unique values
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V54numeric832 unique values
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V55numeric775 unique values
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V56numeric699 unique values
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V57numeric669 unique values
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V59numeric581 unique values
0 missing
V60numeric547 unique values
0 missing
V61numeric513 unique values
0 missing
V62numeric454 unique values
0 missing
V63numeric433 unique values
0 missing
V64numeric411 unique values
0 missing
V65numeric410 unique values
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V66numeric416 unique values
0 missing
V67numeric511 unique values
0 missing
V68numeric1067 unique values
0 missing
V69numeric1511 unique values
0 missing
V70numeric2443 unique values
0 missing
V71numeric2292 unique values
0 missing
V72numeric2499 unique values
0 missing
V73numeric2717 unique values
0 missing
V74numeric2880 unique values
0 missing
V75numeric2849 unique values
0 missing
V76numeric2764 unique values
0 missing
V77numeric2617 unique values
0 missing
V78numeric2434 unique values
0 missing
V79numeric2423 unique values
0 missing
V80numeric2224 unique values
0 missing
V81numeric2073 unique values
0 missing
V82numeric1866 unique values
0 missing
V83numeric1859 unique values
0 missing
V84numeric1493 unique values
0 missing
V85numeric29467 unique values
0 missing
V86numeric29768 unique values
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V87numeric29959 unique values
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V88numeric29768 unique values
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V89numeric38508 unique values
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V90numeric38849 unique values
0 missing
V91numeric38716 unique values
0 missing
V92numeric38849 unique values
0 missing
V93numeric8538 unique values
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V94numeric9769 unique values
0 missing
V95numeric8480 unique values
0 missing
V96numeric9769 unique values
0 missing
V97numeric8322 unique values
0 missing
V98numeric6497 unique values
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V99numeric8272 unique values
0 missing
V100numeric6497 unique values
0 missing
V101numeric18735 unique values
0 missing
V102numeric20006 unique values
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V103numeric18214 unique values
0 missing
V104numeric20006 unique values
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V105numeric36865 unique values
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V106numeric39120 unique values
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V107numeric35892 unique values
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V108numeric39120 unique values
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V109numeric3159 unique values
0 missing
V110numeric1295 unique values
0 missing
V111numeric1539 unique values
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V112numeric1696 unique values
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V113numeric1678 unique values
0 missing
V114numeric1873 unique values
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V115numeric1382 unique values
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V116numeric1975 unique values
0 missing
V117numeric1595 unique values
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V118numeric1354 unique values
0 missing
V119numeric2341 unique values
0 missing
V120numeric1273 unique values
0 missing
V121numeric1537 unique values
0 missing
V122numeric1783 unique values
0 missing
V123numeric1627 unique values
0 missing
V124numeric1532 unique values
0 missing
V125numeric1285 unique values
0 missing
V126numeric2630 unique values
0 missing
V127numeric2015 unique values
0 missing
V128numeric1365 unique values
0 missing
V129numeric1626 unique values
0 missing
V130numeric1685 unique values
0 missing
V131numeric1843 unique values
0 missing
V132numeric1668 unique values
0 missing
V133numeric1499 unique values
0 missing
V134numeric2394 unique values
0 missing
V135numeric1632 unique values
0 missing
V136numeric1791 unique values
0 missing
V137numeric2084 unique values
0 missing
V138numeric1813 unique values
0 missing
V139numeric2156 unique values
0 missing
V140numeric2091 unique values
0 missing
V141numeric2193 unique values
0 missing
V142numeric2247 unique values
0 missing
V143numeric2062 unique values
0 missing
V144numeric3427 unique values
0 missing
V145numeric2793 unique values
0 missing
V146numeric2029 unique values
0 missing
V147numeric2211 unique values
0 missing
V148numeric2194 unique values
0 missing
V149numeric2092 unique values
0 missing
V150numeric2193 unique values
0 missing
V151numeric1804 unique values
0 missing
V152numeric2078 unique values
0 missing
V153numeric1765 unique values
0 missing
V154numeric1607 unique values
0 missing
V155numeric2372 unique values
0 missing
V156numeric1493 unique values
0 missing
V157numeric1639 unique values
0 missing
V158numeric1812 unique values
0 missing
V159numeric1684 unique values
0 missing
V160numeric1589 unique values
0 missing
V161numeric1362 unique values
0 missing
V162numeric2576 unique values
0 missing
V163numeric2083 unique values
0 missing
V164numeric1306 unique values
0 missing
V165numeric1533 unique values
0 missing
V166numeric1627 unique values
0 missing
V167numeric1773 unique values
0 missing
V168numeric1482 unique values
0 missing
V169numeric1250 unique values
0 missing
V170numeric2355 unique values
0 missing
V171numeric1298 unique values
0 missing
V172numeric1554 unique values
0 missing
V173numeric1949 unique values
0 missing
V174numeric1366 unique values
0 missing
V175numeric1845 unique values
0 missing
V176numeric1655 unique values
0 missing
V177numeric1669 unique values
0 missing
V178numeric1508 unique values
0 missing
V179numeric1255 unique values
0 missing
V180numeric3819 unique values
0 missing

62 properties

58310
Number of instances (rows) of the dataset.
181
Number of attributes (columns) of the dataset.
10
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.
180
Number of numeric attributes.
1
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
10
Average number of distinct values among the attributes of the nominal type.
3.59
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.
3.17
Mean skewness among attributes of the numeric type.
0.01
Second quartile (Median) of means among attributes of the numeric type.
21.96
Percentage of instances belonging to the most frequent class.
0.07
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
12806
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.15
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.23
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.01
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
1338.97
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
19.12
Third quartile of kurtosis among attributes of the numeric type.
1.6
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.
0.04
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
10
The minimal number of distinct values among attributes of the nominal type.
99.45
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
10
The maximum number of distinct values among attributes of the nominal type.
-1.39
Minimum skewness among attributes of the numeric type.
0.55
Percentage of nominal attributes.
3.91
Third quartile of skewness among attributes of the numeric type.
28.83
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.08
Third quartile of standard deviation of attributes of the numeric type.
0.85
Maximum standard deviation of attributes of the numeric type.
2.33
Percentage of instances belonging to the least frequent class.
1.28
First quartile of kurtosis 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.
1361
Number of instances belonging to the least frequent class.
0
First quartile of means among attributes of the numeric type.
47.91
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.
0.13
Mean of means among attributes of the numeric type.
0.37
First quartile of skewness among attributes of the numeric type.
0.15
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0
First quartile of standard deviation of attributes of the numeric type.
2.96
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

7 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_class_complexity - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 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