Task
Supervised Regression on lowbwt

Supervised Regression on lowbwt

Task 2294 Supervised Regression lowbwt 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 lowbwt
0 likes - 0 downloads - 0 reach - mean_absolute_error: 331.8939, mean_prior_absolute_error: 591.027, number_of_instances: 189, relative_absolute_error: 0.5616, root_mean_prior_squared_error: 727.0912, root_mean_squared_error: 428.1616, root_relative_squared_error: 0.5889, usercpu_time_millis: 0.256, usercpu_time_millis_testing: 0.096, usercpu_time_millis_training: 0.16,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart(5) on task Supervised Regression on data set lowbwt
0 likes - 0 downloads - 0 reach - mean_absolute_error: 331.8939, mean_prior_absolute_error: 591.027, number_of_instances: 189, relative_absolute_error: 0.5616, root_mean_prior_squared_error: 727.0912, root_mean_squared_error: 428.1616, root_relative_squared_error: 0.5889, usercpu_time_millis: 0.072, usercpu_time_millis_testing: 0.026, usercpu_time_millis_training: 0.046,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart.dummied(1) on task Supervised Regression on data set lowbwt
0 likes - 0 downloads - 0 reach - mean_absolute_error: 331.8939, mean_prior_absolute_error: 591.027, number_of_instances: 189, relative_absolute_error: 0.5616, root_mean_prior_squared_error: 727.0912, root_mean_squared_error: 428.1616, root_relative_squared_error: 0.5889, usercpu_time_millis: 0.212, usercpu_time_millis_testing: 0.074, usercpu_time_millis_training: 0.138,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart(5) on task Supervised Regression on data set lowbwt
0 likes - 0 downloads - 0 reach - mean_absolute_error: 331.8939, mean_prior_absolute_error: 591.027, number_of_instances: 189, relative_absolute_error: 0.5616, root_mean_prior_squared_error: 727.0912, root_mean_squared_error: 428.1616, root_relative_squared_error: 0.5889, usercpu_time_millis: 0.204, usercpu_time_millis_testing: 0.026, usercpu_time_millis_training: 0.178,

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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 lowbwt (1)
    target_feature class

    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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