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christine

christine

active ARFF Publicly available Visibility: public Uploaded 15-08-2018 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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V633numeric672 unique values
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V1145numeric399 unique values
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0 missing
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V1146numeric309 unique values
0 missing
V1402numeric556 unique values
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0 missing
V379numeric597 unique values
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V891numeric179 unique values
0 missing
V1147numeric229 unique values
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V1403numeric697 unique values
0 missing
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V380numeric665 unique values
0 missing
V636numeric100 unique values
0 missing
V892numeric334 unique values
0 missing
V1148numeric583 unique values
0 missing
V1404numeric531 unique values
0 missing
V125numeric24 unique values
0 missing
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V637numeric417 unique values
0 missing
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0 missing
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0 missing
V1405numeric493 unique values
0 missing
V126numeric35 unique values
0 missing
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0 missing
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0 missing
V894numeric474 unique values
0 missing
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0 missing
V1406numeric511 unique values
0 missing
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0 missing
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0 missing
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0 missing
V1151numeric185 unique values
0 missing
V1407numeric328 unique values
0 missing
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V640numeric471 unique values
0 missing
V896numeric578 unique values
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0 missing
V385numeric515 unique values
0 missing
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0 missing
V897numeric638 unique values
0 missing
V1153numeric469 unique values
0 missing
V1409numeric555 unique values
0 missing
V130numeric589 unique values
0 missing
V386numeric516 unique values
0 missing
V642numeric60 unique values
0 missing
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0 missing
V1154numeric412 unique values
0 missing
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0 missing
V131numeric213 unique values
0 missing
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0 missing
V643numeric214 unique values
0 missing
V899numeric519 unique values
0 missing
V1155numeric399 unique values
0 missing
V1411numeric415 unique values
0 missing
V132numeric398 unique values
0 missing
V388numeric58 unique values
0 missing
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0 missing
V900numeric625 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
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0 missing
V1414numeric526 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
V1416numeric448 unique values
0 missing
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0 missing
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0 missing
V649numeric740 unique values
0 missing
V905numeric454 unique values
0 missing
V1161numeric184 unique values
0 missing
V1417numeric320 unique values
0 missing
V138numeric169 unique values
0 missing
V394numeric301 unique values
0 missing
V650numeric465 unique values
0 missing
V906numeric521 unique values
0 missing
V1162numeric503 unique values
0 missing
V1418numeric594 unique values
0 missing
V139numeric436 unique values
0 missing
V395numeric488 unique values
0 missing
V651numeric447 unique values
0 missing
V907numeric401 unique values
0 missing
V1163numeric317 unique values
0 missing
V1419numeric517 unique values
0 missing
V140numeric366 unique values
0 missing
V396numeric319 unique values
0 missing
V652numeric27 unique values
0 missing
V908numeric640 unique values
0 missing
V1164numeric301 unique values
0 missing
V1420numeric208 unique values
0 missing
V141numeric520 unique values
0 missing
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0 missing
V653numeric555 unique values
0 missing
V909numeric474 unique values
0 missing
V1165numeric602 unique values
0 missing
V1421numeric232 unique values
0 missing
V142numeric594 unique values
0 missing
V398numeric561 unique values
0 missing
V654numeric462 unique values
0 missing
V910numeric469 unique values
0 missing
V1166numeric41 unique values
0 missing
V1422numeric548 unique values
0 missing
V143numeric389 unique values
0 missing
V399numeric446 unique values
0 missing
V655numeric417 unique values
0 missing
V911numeric522 unique values
0 missing
V1167numeric635 unique values
0 missing
V1423numeric380 unique values
0 missing
V144numeric179 unique values
0 missing
V400numeric33 unique values
0 missing
V656numeric528 unique values
0 missing
V912numeric350 unique values
0 missing
V1168numeric212 unique values
0 missing
V1424numeric467 unique values
0 missing
V145numeric507 unique values
0 missing
V401numeric383 unique values
0 missing
V657numeric363 unique values
0 missing
V913numeric549 unique values
0 missing
V1169numeric447 unique values
0 missing
V1425numeric523 unique values
0 missing
V146numeric362 unique values
0 missing
V402numeric378 unique values
0 missing
V658numeric377 unique values
0 missing
V914numeric551 unique values
0 missing
V1170numeric171 unique values
0 missing
V1426numeric79 unique values
0 missing
V147numeric296 unique values
0 missing
V403numeric348 unique values
0 missing
V659numeric38 unique values
0 missing
V915numeric99 unique values
0 missing
V1171numeric536 unique values
0 missing
V1427numeric500 unique values
0 missing
V148numeric537 unique values
0 missing
V404numeric396 unique values
0 missing
V660numeric561 unique values
0 missing
V916numeric69 unique values
0 missing
V1172numeric212 unique values
0 missing
V1428numeric403 unique values
0 missing
V149numeric492 unique values
0 missing
V405numeric16 unique values
0 missing
V661numeric497 unique values
0 missing
V917numeric422 unique values
0 missing
V1173numeric92 unique values
0 missing

62 properties

5418
Number of instances (rows) of the dataset.
1637
Number of attributes (columns) of the dataset.
2
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.
1599
Number of numeric attributes.
38
Number of nominal attributes.
0.3
Number of attributes divided by the number of instances.
1.34
Average number of distinct values among the attributes of the nominal type.
1.28
Second quartile (Median) of kurtosis among attributes of the numeric type.
3250.7
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
1.19
Mean skewness among attributes of the numeric type.
254.89
Second quartile (Median) of means among attributes of the numeric type.
50
Percentage of instances belonging to the most frequent class.
87.32
Mean standard deviation of attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
2709
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
0.6
Second quartile (Median) of skewness among attributes of the numeric type.
0.45
Maximum entropy among attributes.
-1.71
Minimum kurtosis among attributes of the numeric type.
0.79
Percentage of binary attributes.
85
Second quartile (Median) of standard deviation of attributes of the numeric type.
3692.49
Maximum kurtosis among attributes of the numeric type.
0.21
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
0
Third quartile of entropy among attributes.
984.17
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
5.13
Third quartile of kurtosis among attributes of the numeric type.
0.01
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
97.68
Percentage of numeric attributes.
367.41
Third quartile of means among attributes of the numeric type.
2
The maximum number of distinct values among attributes of the nominal type.
-11.99
Minimum skewness among attributes of the numeric type.
2.32
Percentage of nominal attributes.
0
Third quartile of mutual information between the nominal attributes and the target attribute.
58.89
Maximum skewness among attributes of the numeric type.
0.47
Minimum standard deviation of attributes of the numeric type.
0
First quartile of entropy among attributes.
1.4
Third quartile of skewness among attributes of the numeric type.
297.63
Maximum standard deviation of attributes of the numeric type.
50
Percentage of instances belonging to the least frequent class.
0.26
First quartile of kurtosis among attributes of the numeric type.
109.36
Third quartile of standard deviation of attributes of the numeric type.
0.08
Average entropy of the attributes.
2709
Number of instances belonging to the least frequent class.
144.4
First quartile of means among attributes of the numeric type.
0.48
Standard deviation of the number of distinct values among attributes of the nominal type.
22.49
Mean kurtosis among attributes of the numeric type.
13
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
283.63
Mean of means among attributes of the numeric type.
0.1
First quartile of skewness among attributes of the numeric type.
0.5
Average class difference between consecutive instances.
0
Average mutual information between the nominal attributes and the target attribute.
63.62
First quartile of standard deviation of attributes of the numeric type.
1
Entropy of the target attribute values.
251.85
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
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 - evaluation_measure: area_under_roc_curve - 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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