Task
Supervised Regression on diamonds

Supervised Regression on diamonds

Task 233211 Supervised Regression diamonds 5 runs submitted
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Sergey Redyuk 3 years ago
ran flow autosklearn.estimators.AutoSklearnRegressor(1) on task Supervised Regression on data set diamonds
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 3031.6032, number_of_instances: 53940, root_mean_prior_squared_error: 3989.4028,
Sergey Redyuk 3 years ago
ran flow autosklearn.estimators.AutoSklearnRegressor(1) on task Supervised Regression on data set diamonds
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 3031.6032, number_of_instances: 53940, root_mean_prior_squared_error: 3989.4028,
Sergey Redyuk 3 years ago
ran flow sklearn.pipeline.Pipeline(easy ... abl.models.SimpleRegressor)(1) on task Supervised Regression on data set diamonds
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 3031.6032, number_of_instances: 53940, root_mean_prior_squared_error: 3989.4028,
Sebastian Fischer 3 years ago
ran flow mlr3.regr.rpart(1) on task Supervised Regression on data set diamonds
0 likes - 0 downloads - 0 reach - mean_absolute_error: 854.817, mean_prior_absolute_error: 3031.6032, number_of_instances: 53940, relative_absolute_error: 0.282, root_mean_prior_squared_error: 3989.4028, root_mean_squared_error: 1312.6536, root_relative_squared_error: 0.329,
Sebastian Fischer 3 years ago
ran flow mlr3.regr.rpart(1) on task Supervised Regression on data set diamonds
0 likes - 0 downloads - 0 reach - No evaluations yet (or not applicable).

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    Leaderboard

    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.

    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 root_mean_squared_error
    source_data diamonds (1)
    target_feature price

    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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