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jannis

jannis

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

55 features

class (target)nominal4 unique values
0 missing
V1numeric49883 unique values
0 missing
V2numeric854 unique values
0 missing
V3numeric871 unique values
0 missing
V4numeric78429 unique values
0 missing
V5numeric80119 unique values
0 missing
V6numeric77198 unique values
0 missing
V7numeric79001 unique values
0 missing
V8numeric78272 unique values
0 missing
V9numeric76727 unique values
0 missing
V10numeric72445 unique values
0 missing
V11numeric73939 unique values
0 missing
V12numeric76580 unique values
0 missing
V13numeric78893 unique values
0 missing
V14numeric78948 unique values
0 missing
V15numeric79167 unique values
0 missing
V16numeric81910 unique values
0 missing
V17numeric81865 unique values
0 missing
V18numeric81469 unique values
0 missing
V19numeric77497 unique values
0 missing
V20numeric82216 unique values
0 missing
V21numeric81534 unique values
0 missing
V22numeric75989 unique values
0 missing
V23numeric80108 unique values
0 missing
V24numeric80124 unique values
0 missing
V25numeric82018 unique values
0 missing
V26numeric81783 unique values
0 missing
V27numeric81574 unique values
0 missing
V28numeric77513 unique values
0 missing
V29numeric857 unique values
0 missing
V30numeric82218 unique values
0 missing
V31numeric79105 unique values
0 missing
V32numeric78979 unique values
0 missing
V33numeric75973 unique values
0 missing
V34numeric78980 unique values
0 missing
V35numeric82248 unique values
0 missing
V36numeric81452 unique values
0 missing
V37numeric72571 unique values
0 missing
V38numeric869 unique values
0 missing
V39numeric72535 unique values
0 missing
V40numeric79167 unique values
0 missing
V41numeric81830 unique values
0 missing
V42numeric79012 unique values
0 missing
V43numeric76026 unique values
0 missing
V44numeric73851 unique values
0 missing
V45numeric78963 unique values
0 missing
V46numeric78410 unique values
0 missing
V47numeric76748 unique values
0 missing
V48numeric78323 unique values
0 missing
V49numeric72534 unique values
0 missing
V50numeric81484 unique values
0 missing
V51numeric79239 unique values
0 missing
V52numeric76003 unique values
0 missing
V53numeric50002 unique values
0 missing
V54numeric77225 unique values
0 missing

62 properties

83733
Number of instances (rows) of the dataset.
55
Number of attributes (columns) of the dataset.
4
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.
54
Number of numeric attributes.
1
Number of nominal attributes.
0.25
First quartile of standard deviation of attributes of the numeric type.
0.36
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
1.6
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.
-0.23
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
4
Average number of distinct values among the attributes of the nominal type.
0.82
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.
-0.28
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
46.01
Percentage of instances belonging to the most frequent class.
12.04
Mean standard deviation of attributes of the numeric type.
0.37
Second quartile (Median) of skewness among attributes of the numeric type.
38522
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
3.38
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.59
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
216.16
Maximum kurtosis among attributes of the numeric type.
-0.75
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
6.59
Third quartile of kurtosis among attributes of the numeric type.
125.44
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
98.18
Percentage of numeric attributes.
34.51
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
4
The minimal number of distinct values among attributes of the nominal type.
1.82
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
4
The maximum number of distinct values among attributes of the nominal type.
-10.43
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
1.01
Third quartile of skewness among attributes of the numeric type.
2.48
Maximum skewness among attributes of the numeric type.
0.04
Minimum standard deviation of attributes of the numeric type.
-0.46
First quartile of kurtosis among attributes of the numeric type.
18.64
Third quartile of standard deviation of attributes of the numeric type.
60.75
Maximum standard deviation of attributes of the numeric type.
2.01
Percentage of instances belonging to the least frequent class.
0.36
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.
1687
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
20.29
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
0.13
First quartile of skewness among attributes of the numeric type.
24.8
Mean of means among attributes of the numeric type.

4 tasks

0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_class_complexity - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Define a new task