Task
Supervised Regression on analcatdata_vineyard

Supervised Regression on analcatdata_vineyard

Task 4830 Supervised Regression analcatdata_vineyard 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_vineyard
0 likes - 0 downloads - 0 reach - mean_absolute_error: 2.2014, mean_prior_absolute_error: 4.2648, number_of_instances: 468, relative_absolute_error: 0.5162, root_mean_prior_squared_error: 5.2932, root_mean_squared_error: 2.8799, root_relative_squared_error: 0.5441, usercpu_time_millis: 0.155, usercpu_time_millis_testing: 0.032, usercpu_time_millis_training: 0.123,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart(5) on task Supervised Regression on data set analcatdata_vineyard
0 likes - 0 downloads - 0 reach - mean_absolute_error: 2.2057, mean_prior_absolute_error: 4.2648, number_of_instances: 468, relative_absolute_error: 0.5172, root_mean_prior_squared_error: 5.2932, root_mean_squared_error: 2.8762, root_relative_squared_error: 0.5434, usercpu_time_millis: 0.047, usercpu_time_millis_testing: 0.015, usercpu_time_millis_training: 0.032,

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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_vineyard (1)
    target_feature Lugs

    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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