Task
Supervised Regression on analcatdata_apnea1

Supervised Regression on analcatdata_apnea1

Task 4880 Supervised Regression analcatdata_apnea1 2 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 analcatdata_apnea1
0 likes - 0 downloads - 0 reach - mean_absolute_error: 464.1607, mean_prior_absolute_error: 1374.4617, number_of_instances: 475, relative_absolute_error: 0.3377, root_mean_prior_squared_error: 3184.0108, root_mean_squared_error: 1046.7062, root_relative_squared_error: 0.3287, usercpu_time_millis: 0.145, usercpu_time_millis_testing: 0.042, usercpu_time_millis_training: 0.103,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart(5) on task Supervised Regression on data set analcatdata_apnea1
0 likes - 0 downloads - 0 reach - mean_absolute_error: 464.1607, mean_prior_absolute_error: 1374.4617, number_of_instances: 475, relative_absolute_error: 0.3377, root_mean_prior_squared_error: 3184.0108, root_mean_squared_error: 1046.7062, root_relative_squared_error: 0.3287, usercpu_time_millis: 0.054, usercpu_time_millis_testing: 0.018, usercpu_time_millis_training: 0.036,

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    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 analcatdata_apnea1 (1)
    target_feature Count

    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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