Data
dionis

dionis

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

61 features

class (target)nominal355 unique values
0 missing
V1numeric4925 unique values
0 missing
V2numeric8044 unique values
0 missing
V3numeric1309 unique values
0 missing
V4numeric58054 unique values
0 missing
V5numeric4087 unique values
0 missing
V6numeric1182 unique values
0 missing
V7numeric20836 unique values
0 missing
V8numeric61135 unique values
0 missing
V9numeric4979 unique values
0 missing
V10numeric1886 unique values
0 missing
V11numeric9314 unique values
0 missing
V12numeric34664 unique values
0 missing
V13numeric16974 unique values
0 missing
V14numeric1 unique values
0 missing
V15numeric58575 unique values
0 missing
V16numeric8624 unique values
0 missing
V17numeric9123 unique values
0 missing
V18numeric41067 unique values
0 missing
V19numeric7747 unique values
0 missing
V20numeric3815 unique values
0 missing
V21numeric2967 unique values
0 missing
V22numeric7276 unique values
0 missing
V23numeric82253 unique values
0 missing
V24numeric40314 unique values
0 missing
V25numeric1459 unique values
0 missing
V26numeric2620 unique values
0 missing
V27numeric1 unique values
0 missing
V28numeric11383 unique values
0 missing
V29numeric9674 unique values
0 missing
V30numeric49007 unique values
0 missing
V31numeric6888 unique values
0 missing
V32numeric7478 unique values
0 missing
V33numeric1 unique values
0 missing
V34numeric8279 unique values
0 missing
V35numeric1 unique values
0 missing
V36numeric4925 unique values
0 missing
V37numeric1 unique values
0 missing
V38numeric16869 unique values
0 missing
V39numeric6229 unique values
0 missing
V40numeric30659 unique values
0 missing
V41numeric66076 unique values
0 missing
V42numeric52856 unique values
0 missing
V43numeric34775 unique values
0 missing
V44numeric4541 unique values
0 missing
V45numeric2254 unique values
0 missing
V46numeric58989 unique values
0 missing
V47numeric6838 unique values
0 missing
V48numeric59559 unique values
0 missing
V49numeric1694 unique values
0 missing
V50numeric62447 unique values
0 missing
V51numeric8165 unique values
0 missing
V52numeric4471 unique values
0 missing
V53numeric38 unique values
0 missing
V54numeric1 unique values
0 missing
V55numeric8499 unique values
0 missing
V56numeric63618 unique values
0 missing
V57numeric55797 unique values
0 missing
V58numeric5006 unique values
0 missing
V59numeric22748 unique values
0 missing
V60numeric1565 unique values
0 missing

62 properties

416188
Number of instances (rows) of the dataset.
61
Number of attributes (columns) of the dataset.
355
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.
60
Number of numeric attributes.
1
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
355
Average number of distinct values among the attributes of the nominal type.
1.8
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.
-40
Mean skewness among attributes of the numeric type.
82.27
Second quartile (Median) of means among attributes of the numeric type.
0.59
Percentage of instances belonging to the most frequent class.
730917.26
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
2469
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.14
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-0.57
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
1451.96
Second quartile (Median) of standard deviation of attributes of the numeric type.
369550.74
Maximum kurtosis among attributes of the numeric type.
-8372.24
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
10070.77
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.
199587.81
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
355
The minimal number of distinct values among attributes of the nominal type.
98.36
Percentage of numeric attributes.
3590.37
Third quartile of means among attributes of the numeric type.
355
The maximum number of distinct values among attributes of the nominal type.
-586
Minimum skewness among attributes of the numeric type.
1.64
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
565.38
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.47
Third quartile of skewness among attributes of the numeric type.
4007456.75
Maximum standard deviation of attributes of the numeric type.
0.21
Percentage of instances belonging to the least frequent class.
0.82
First quartile of kurtosis among attributes of the numeric type.
798265.78
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
878
Number of instances belonging to the least frequent class.
-88.18
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.
77960.01
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.
1199.26
Mean of means among attributes of the numeric type.
-1.7
First quartile of skewness among attributes of the numeric type.
0
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
412.69
First quartile of standard deviation of attributes of the numeric type.
8.45
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.

6 tasks

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
Define a new task