Data
rl

rl

active ARFF Publicly available Visibility: public Uploaded 12-07-2022 by Leo Grin
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Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on categorical and numerical features" benchmark. Original description: 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

13 features

class (target)nominal2 unique values
0 missing
V1numeric392 unique values
0 missing
V5numeric62 unique values
0 missing
V6numeric55 unique values
0 missing
V8nominal2 unique values
0 missing
V14nominal3 unique values
0 missing
V15nominal1 unique values
0 missing
V17nominal3 unique values
0 missing
V18nominal2 unique values
0 missing
V19nominal2 unique values
0 missing
V20numeric400 unique values
0 missing
V21numeric377 unique values
0 missing
V22nominal20 unique values
0 missing

19 properties

4970
Number of instances (rows) of the dataset.
13
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.
5
Number of numeric attributes.
8
Number of nominal attributes.
4
Number of binary attributes.
30.77
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
1
Average class difference between consecutive instances.
38.46
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
61.54
Percentage of nominal attributes.
50
Percentage of instances belonging to the most frequent class.
2485
Number of instances belonging to the most frequent class.
50
Percentage of instances belonging to the least frequent class.
2485
Number of instances belonging to the least frequent class.

1 tasks

1 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
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