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sylvine

active
ARFF
Publicly available Visibility: public Uploaded 15-08-2018 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

class (target) | nominal | 2 unique values 0 missing | |

V1 | numeric | 157 unique values 0 missing | |

V2 | numeric | 364 unique values 0 missing | |

V3 | numeric | 118 unique values 0 missing | |

V4 | numeric | 2549 unique values 0 missing | |

V5 | numeric | 360 unique values 0 missing | |

V6 | numeric | 2574 unique values 0 missing | |

V7 | numeric | 1183 unique values 0 missing | |

V8 | numeric | 48 unique values 0 missing | |

V9 | numeric | 2620 unique values 0 missing | |

V10 | numeric | 389 unique values 0 missing | |

V11 | numeric | 120 unique values 0 missing | |

V12 | numeric | 151 unique values 0 missing | |

V13 | numeric | 146 unique values 0 missing | |

V14 | numeric | 2556 unique values 0 missing | |

V15 | numeric | 2218 unique values 0 missing | |

V16 | numeric | 393 unique values 0 missing | |

V17 | numeric | 122 unique values 0 missing | |

V18 | numeric | 360 unique values 0 missing | |

V19 | numeric | 361 unique values 0 missing | |

V20 | numeric | 226 unique values 0 missing |

90.67

Second quartile (Median) of standard deviation of attributes of the numeric type.

1.83

Third quartile of kurtosis among attributes of the numeric type.

Minimal mutual information between the nominal attributes and the target attribute.

Maximum mutual information between the nominal attributes and the target attribute.

2

The minimal number of distinct values among attributes of the nominal type.

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.

0.89

Third quartile of skewness among attributes of the numeric type.

-0.43

First quartile of kurtosis among attributes of the numeric type.

980.45

Third quartile of standard deviation of attributes of the numeric type.

0

Standard deviation of the number of distinct values among attributes of the nominal type.

First quartile of mutual information between the nominal attributes and the target attribute.

-1.05

First quartile of skewness among attributes of the numeric type.

25.37

First quartile of standard deviation of attributes of the numeric type.

Average mutual information between the nominal attributes and the target attribute.

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

1.03

Second quartile (Median) of kurtosis among attributes of the numeric type.

2

Average number of distinct values among the attributes of the nominal type.

218.47

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.

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

0.44

Second quartile (Median) of skewness among attributes of the numeric type.