Task
Supervised Regression on arsenic-female-bladder

Supervised Regression on arsenic-female-bladder

Task 4858 Supervised Regression arsenic-female-bladder 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 arsenic-female-bladder
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.9681, mean_prior_absolute_error: 1.4808, number_of_instances: 559, relative_absolute_error: 0.6538, root_mean_prior_squared_error: 6.2371, root_mean_squared_error: 5.0647, root_relative_squared_error: 0.812, usercpu_time_millis: 0.392, usercpu_time_millis_testing: 0.072, usercpu_time_millis_training: 0.32,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart(5) on task Supervised Regression on data set arsenic-female-bladder
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.9698, mean_prior_absolute_error: 1.4808, number_of_instances: 559, relative_absolute_error: 0.6549, root_mean_prior_squared_error: 6.2371, root_mean_squared_error: 5.0716, root_relative_squared_error: 0.8131, usercpu_time_millis: 0.051, usercpu_time_millis_testing: 0.014, usercpu_time_millis_training: 0.037,

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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
    evaluation_measures mean_absolute_error
    source_data arsenic-female-bladder (1)
    target_feature events

    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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