Task
Supervised Regression on pm10

Supervised Regression on pm10

Task 4848 Supervised Regression pm10 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 pm10
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5704, mean_prior_absolute_error: 0.6827, number_of_instances: 500, relative_absolute_error: 0.8356, root_mean_prior_squared_error: 0.8859, root_mean_squared_error: 0.7478, root_relative_squared_error: 0.8441,
Nico Strasdat 6 years ago
ran flow sklearn.pipeline.Pipeline(miss ... er,predict=hpo4svm.hpo.SVR)(1) on task Supervised Regression on data set pm10
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5745, mean_prior_absolute_error: 0.6827, number_of_instances: 500, relative_absolute_error: 0.8415, root_mean_prior_squared_error: 0.8859, root_mean_squared_error: 0.7483, root_relative_squared_error: 0.8446,

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

    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 pm10 (1)
    target_feature pm10_concentration

    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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