Task
Supervised Regression on HappinessRank_2015

Supervised Regression on HappinessRank_2015

Task 146685 Supervised Regression HappinessRank_2015 2 runs submitted
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Qing Cai 7 years ago
ran flow sklearn.ensemble.forest.RandomForestRegressor(1) on task Supervised Regression on data set HappinessRank_2015
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.0414, mean_prior_absolute_error: 0.9584, number_of_instances: 158, relative_absolute_error: 0.0432, root_mean_prior_squared_error: 1.1414, root_mean_squared_error: 0.0653, root_relative_squared_error: 0.0572,
Qing Cai 7 years ago
ran flow sklearn.ensemble.forest.RandomForestRegressor(1) on task Supervised Regression on data set HappinessRank_2015
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.0428, mean_prior_absolute_error: 0.9584, number_of_instances: 158, relative_absolute_error: 0.0446, root_mean_prior_squared_error: 1.1414, root_mean_squared_error: 0.0771, root_relative_squared_error: 0.0675,

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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
    source_data HappinessRank_2015 (1)
    target_feature Happiness Score

    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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