Data
rl

rl

active ARFF Publicly available Visibility: public Uploaded 16-08-2018 by Janek Thomas
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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

23 features

class (target)nominal2 unique values
0 missing
V1numeric580 unique values
40 missing
V2numeric9 unique values
1715 missing
V3numeric1140 unique values
10182 missing
V4numeric160 unique values
16279 missing
V5numeric71 unique values
0 missing
V6numeric57 unique values
0 missing
V7nominal2580 unique values
0 missing
V8nominal3 unique values
0 missing
V9nominal857 unique values
0 missing
V10nominal1609 unique values
0 missing
V11nominal28 unique values
0 missing
V12nominal1199 unique values
0 missing
V13nominal24 unique values
0 missing
V14nominal3 unique values
0 missing
V15nominal1 unique values
0 missing
V16nominal1097 unique values
0 missing
V17nominal4 unique values
0 missing
V18nominal3 unique values
0 missing
V19nominal3 unique values
0 missing
V20numeric406 unique values
40 missing
V21numeric431 unique values
1500 missing
V22nominal20 unique values
0 missing

62 properties

31406
Number of instances (rows) of the dataset.
23
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
29756
Number of missing values in the dataset.
17204
Number of instances with at least one value missing.
8
Number of numeric attributes.
15
Number of nominal attributes.
226.19
Third quartile of means among attributes of the numeric type.
0.28
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
34.78
Percentage of numeric attributes.
0.03
Third quartile of mutual information between the nominal attributes and the target attribute.
2580
The maximum number of distinct values among attributes of the nominal type.
-0.48
Minimum skewness among attributes of the numeric type.
65.22
Percentage of nominal attributes.
2.71
Third quartile of skewness among attributes of the numeric type.
3.49
Maximum skewness among attributes of the numeric type.
6.18
Minimum standard deviation of attributes of the numeric type.
0.96
First quartile of entropy among attributes.
143.33
Third quartile of standard deviation of attributes of the numeric type.
448.05
Maximum standard deviation of attributes of the numeric type.
9.54
Percentage of instances belonging to the least frequent class.
-0.8
First quartile of kurtosis among attributes of the numeric type.
799.07
Standard deviation of the number of distinct values among attributes of the nominal type.
3.73
Average entropy of the attributes.
2995
Number of instances belonging to the least frequent class.
21.56
First quartile of means among attributes of the numeric type.
6.25
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
347.84
Mean of means among attributes of the numeric type.
0.56
First quartile of skewness among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.03
Average mutual information between the nominal attributes and the target attribute.
9.73
First quartile of standard deviation of attributes of the numeric type.
0.45
Entropy of the target attribute values.
106.28
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2.11
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
495.53
Average number of distinct values among the attributes of the nominal type.
3.75
Second quartile (Median) of kurtosis among attributes of the numeric type.
13.05
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
1.7
Mean skewness among attributes of the numeric type.
149.35
Second quartile (Median) of means among attributes of the numeric type.
90.46
Percentage of instances belonging to the most frequent class.
115.78
Mean standard deviation of attributes of the numeric type.
0.02
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
28411
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
1.85
Second quartile (Median) of skewness among attributes of the numeric type.
9.72
Maximum entropy among attributes.
-1.45
Minimum kurtosis among attributes of the numeric type.
4.35
Percentage of binary attributes.
83.98
Second quartile (Median) of standard deviation of attributes of the numeric type.
7.25
Third quartile of entropy among attributes.
20.62
Maximum kurtosis among attributes of the numeric type.
7
Minimum of means among attributes of the numeric type.
54.78
Percentage of instances having missing values.
13.29
Third quartile of kurtosis among attributes of the numeric type.
1978.97
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
4.12
Percentage of missing values.

5 tasks

0 runs - estimation_procedure: 33% Holdout set - 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