Task

Supervised Classification on rabe_176

Task 4497 Supervised Classification
rabe_176
1 runs submitted

0 likes downloaded by 0 people , 0 total downloads 0 issues

Visibility: Public

0 likes downloaded by 0 people , 0 total downloads 0 issues

Visibility: Public

Issue | #Downvotes for this reason | By |
---|

Metric:

Fetching data

Fetching data

Search runs in more detail
**0 likes - 0 downloads - 0 reach ** - area_under_roc_curve: 0.5654, f_measure: 0.5286, kappa: 0.0571, kb_relative_information_score: 0.0729, mean_absolute_error: 0.4645, mean_prior_absolute_error: 0.5, weighted_recall: 0.5286, number_of_instances: 700, precision: 0.5286, predictive_accuracy: 0.5286, prior_entropy: 1, relative_absolute_error: 0.929, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.5479, root_relative_squared_error: 1.0959, scimark_benchmark: 931.1106, unweighted_recall: 0.5286,
**0 likes - 0 downloads - 0 reach ** - area_under_roc_curve: 0.6564, f_measure: 0.6229, kappa: 0.2457, kb_relative_information_score: 0.1066, mean_absolute_error: 0.4577, mean_prior_absolute_error: 0.5, weighted_recall: 0.6229, number_of_instances: 700, precision: 0.6229, predictive_accuracy: 0.6229, prior_entropy: 1, relative_absolute_error: 0.9154, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.4897, root_relative_squared_error: 0.9794, scimark_benchmark: 979.7764, unweighted_recall: 0.6229,

Metric:

Plotting contribution timeline

Rank | Name | Top Score | Entries | Highest rank |
---|

Note: The leaderboard ignores resubmissions of previous solutions, as well as parameter variations that do not improve performance.

To make results by different users comparable, you are given the exact train-test folds to be used, and you need to return at least the predictions generated by your model for each of the test instances. OpenML will use these predictions to calculate a range of evaluation measures on the server.

You can also upload your own evaluation measures, provided that the code for doing so is available from the implementation used. For extremely large datasets, it may be infeasible to upload all predictions. In those cases, you need to compute and provide the evaluations yourself.

Optionally, you can upload the model trained on all the input data. There is no restriction on the file format, but please use a well-known format or PMML.

estimation_procedure | 10 times 10-fold Crossvalidation |

evaluation_measures | predictive_accuracy |

source_data | rabe_176 (2) |

target_feature | binaryClass |

evaluations | A list of user-defined evaluations of the task as key-value pairs. | KeyValue (optional) |

model | A file containing the model built on all the input data. | File (optional) |

predictions | The desired output format | Predictions (optional) |

Download this task directly in your environment and automatically upload your results

OpenML bootcampUse one of our APIs to download data from OpenML and upload your results

OpenML APIs