Task
Supervised Regression on elusage

Supervised Regression on elusage

Task 2316 Supervised Regression elusage 4 runs submitted
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Heidi Seibold 7 years ago
ran flow mlr.regr.rpart.dummied(1) on task Supervised Regression on data set elusage
0 likes - 0 downloads - 0 reach - mean_absolute_error: 9.4594, mean_prior_absolute_error: 19.7948, number_of_instances: 55, relative_absolute_error: 0.4779, root_mean_prior_squared_error: 23.7898, root_mean_squared_error: 13.093, root_relative_squared_error: 0.5504, usercpu_time_millis: 0.106, usercpu_time_millis_testing: 0.033, usercpu_time_millis_training: 0.073,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart(5) on task Supervised Regression on data set elusage
0 likes - 0 downloads - 0 reach - mean_absolute_error: 10.3724, mean_prior_absolute_error: 19.7948, number_of_instances: 55, relative_absolute_error: 0.524, root_mean_prior_squared_error: 23.7898, root_mean_squared_error: 14.1464, root_relative_squared_error: 0.5946, usercpu_time_millis: 0.041, usercpu_time_millis_testing: 0.012, usercpu_time_millis_training: 0.029,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart.dummied(1) on task Supervised Regression on data set elusage
0 likes - 0 downloads - 0 reach - mean_absolute_error: 9.4594, mean_prior_absolute_error: 19.7948, number_of_instances: 55, relative_absolute_error: 0.4779, root_mean_prior_squared_error: 23.7898, root_mean_squared_error: 13.093, root_relative_squared_error: 0.5504, usercpu_time_millis: 0.112, usercpu_time_millis_testing: 0.03, usercpu_time_millis_training: 0.082,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart(5) on task Supervised Regression on data set elusage
0 likes - 0 downloads - 0 reach - mean_absolute_error: 10.3724, mean_prior_absolute_error: 19.7948, number_of_instances: 55, relative_absolute_error: 0.524, root_mean_prior_squared_error: 23.7898, root_mean_squared_error: 14.1464, root_relative_squared_error: 0.5946, usercpu_time_millis: 0.038, usercpu_time_millis_testing: 0.013, usercpu_time_millis_training: 0.025,

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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 mean_absolute_error
    source_data elusage (1)
    target_feature average_electricity_usage

    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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