Task
Supervised Regression on liver-disorders

Supervised Regression on liver-disorders

Task 211690 Supervised Regression liver-disorders 5 runs submitted
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jordan porter 3 years ago
ran flow sklearn.model_selection._searc ... _glm.glm.PoissonRegressor))(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.7953, number_of_instances: 113, root_mean_prior_squared_error: 3.5579,
Koralp Catalsakal 5 years ago
ran flow Framework.custom_sklearn_pipel ... arn.decomposition.pca.PCA))(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_absolute_error: 2.4246, mean_prior_absolute_error: 2.7953, number_of_instances: 113, relative_absolute_error: 0.8674, root_mean_prior_squared_error: 3.5579, root_mean_squared_error: 3.1844, root_relative_squared_error: 0.895,
Koralp Catalsakal 5 years ago
ran flow Framework.custom_sklearn_pipel ... ing_blocks.ProcessingBlock)(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_absolute_error: 2.4924, mean_prior_absolute_error: 2.7953, number_of_instances: 113, relative_absolute_error: 0.8916, root_mean_prior_squared_error: 3.5579, root_mean_squared_error: 3.1629, root_relative_squared_error: 0.889,
Koralp Catalsakal 5 years ago
ran flow Framework.custom_sklearn_pipel ... ing_blocks.ProcessingBlock)(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_absolute_error: 2.4997, mean_prior_absolute_error: 2.7953, number_of_instances: 113, relative_absolute_error: 0.8943, root_mean_prior_squared_error: 3.5579, root_mean_squared_error: 3.2329, root_relative_squared_error: 0.9086,
Koralp Catalsakal 5 years ago
ran flow Framework.custom_sklearn_pipel ... ing_blocks.ProcessingBlock)(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_absolute_error: 2.631, mean_prior_absolute_error: 2.7953, number_of_instances: 113, relative_absolute_error: 0.9412, root_mean_prior_squared_error: 3.5579, root_mean_squared_error: 3.3807, root_relative_squared_error: 0.9502,

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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 33% Holdout set
    evaluation_measures root_mean_squared_error
    source_data liver-disorders (1)
    target_feature drinks

    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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