Task
Supervised Regression on abalone

Supervised Regression on abalone

Task 168752 Supervised Regression abalone 2 runs submitted
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Nico Strasdat 6 years ago
ran flow sklearn.pipeline.Pipeline(miss ... er,predict=hpo4svm.hpo.SVR)(1) on task Supervised Regression on data set abalone
0 likes - 0 downloads - 0 reach - mean_absolute_error: 1.4581, mean_prior_absolute_error: 2.3625, number_of_instances: 4177, relative_absolute_error: 0.6172, root_mean_prior_squared_error: 3.2238, root_mean_squared_error: 2.1207, root_relative_squared_error: 0.6578,
Nico Strasdat 6 years ago
ran flow sklearn.pipeline.Pipeline(miss ... er,predict=hpo4svm.hpo.SVR)(1) on task Supervised Regression on data set abalone
0 likes - 0 downloads - 0 reach - mean_absolute_error: 1.4814, mean_prior_absolute_error: 2.3625, number_of_instances: 4177, relative_absolute_error: 0.6271, root_mean_prior_squared_error: 3.2238, root_mean_squared_error: 2.0907, root_relative_squared_error: 0.6485,

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    Challenge

    Given a dataset with a numeric target and a set of train/test splits, e.g. generated by a cross-validation procedure, train a model and return the predictions of that model.

    Given inputs

    estimation_procedure 10-fold Crossvalidation
    evaluation_measures mean_absolute_error
    source_data abalone (4)
    target_feature Number_of_rings

    Expected outputs

    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)

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