Data
riccardo

riccardo

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

4297 features

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V567numeric8185 unique values
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V1591numeric3462 unique values
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V2103numeric9752 unique values
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V3895numeric7808 unique values
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V1592numeric8824 unique values
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V2360numeric6148 unique values
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V2616numeric9155 unique values
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V2872numeric7291 unique values
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V3128numeric5689 unique values
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V57numeric3688 unique values
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V825numeric6070 unique values
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V1081numeric5034 unique values
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V1337numeric9165 unique values
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V1593numeric6023 unique values
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V1849numeric9147 unique values
0 missing
V2105numeric7848 unique values
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V2361numeric9193 unique values
0 missing
V2617numeric8843 unique values
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V2873numeric6391 unique values
0 missing
V3129numeric6322 unique values
0 missing
V3385numeric6401 unique values
0 missing
V3641numeric7306 unique values
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V3897numeric4455 unique values
0 missing
V4153numeric13446 unique values
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V58numeric8206 unique values
0 missing
V314numeric8279 unique values
0 missing
V570numeric7164 unique values
0 missing
V826numeric4983 unique values
0 missing
V1082numeric5297 unique values
0 missing
V1338numeric7682 unique values
0 missing
V1594numeric6715 unique values
0 missing
V1850numeric8910 unique values
0 missing
V2106numeric6333 unique values
0 missing
V2362numeric7260 unique values
0 missing
V2618numeric5606 unique values
0 missing
V2874numeric10015 unique values
0 missing
V3130numeric8792 unique values
0 missing
V3386numeric7805 unique values
0 missing

62 properties

20000
Number of instances (rows) of the dataset.
4297
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.
4296
Number of numeric attributes.
1
Number of nominal attributes.
Second quartile (Median) of entropy among attributes.
0.81
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.
11.35
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.21
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
3.22
Mean skewness among attributes of the numeric type.
0.47
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.
0.92
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
75
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
2.98
Second quartile (Median) of skewness among attributes of the numeric type.
15000
Number of instances belonging to the most frequent class.
0.29
Minimum kurtosis among attributes of the numeric type.
0.02
Percentage of binary attributes.
0.93
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.32
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
8011.78
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
17.61
Third quartile of kurtosis among attributes of the numeric type.
1.8
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
99.98
Percentage of numeric attributes.
0.61
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
-0.49
Minimum skewness among attributes of the numeric type.
0.02
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
0
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
3.64
Third quartile of skewness among attributes of the numeric type.
82.95
Maximum skewness among attributes of the numeric type.
25
Percentage of instances belonging to the least frequent class.
7.63
First quartile of kurtosis among attributes of the numeric type.
1.09
Third quartile of standard deviation of attributes of the numeric type.
2.48
Maximum standard deviation of attributes of the numeric type.
5000
Number of instances belonging to the least frequent class.
0.34
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.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
20.58
Mean kurtosis among attributes of the numeric type.
2.51
First quartile of skewness among attributes of the numeric type.
0.47
Mean of means among attributes of the numeric type.
0.77
First quartile of standard deviation of attributes of the numeric type.
1
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.

8 tasks

5 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: class
0 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