Task
Supervised Regression on fishcatch

Supervised Regression on fishcatch

Task 2320 Supervised Regression fishcatch 10 runs submitted
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Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set fishcatch
0 likes - 0 downloads - 0 reach - mean_absolute_error: 85.2583, mean_prior_absolute_error: 297.3947, number_of_instances: 158, relative_absolute_error: 0.2867, root_mean_prior_squared_error: 357.9481, root_mean_squared_error: 153.7439, root_relative_squared_error: 0.4295,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set fishcatch
0 likes - 0 downloads - 0 reach - mean_absolute_error: 87.7662, mean_prior_absolute_error: 297.3947, number_of_instances: 158, relative_absolute_error: 0.2951, root_mean_prior_squared_error: 357.9481, root_mean_squared_error: 167.204, root_relative_squared_error: 0.4671,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set fishcatch
0 likes - 0 downloads - 0 reach - mean_absolute_error: 79.12, mean_prior_absolute_error: 297.3947, number_of_instances: 158, relative_absolute_error: 0.266, root_mean_prior_squared_error: 357.9481, root_mean_squared_error: 149.7949, root_relative_squared_error: 0.4185,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set fishcatch
0 likes - 0 downloads - 0 reach - mean_absolute_error: 80.1785, mean_prior_absolute_error: 297.3947, number_of_instances: 158, relative_absolute_error: 0.2696, root_mean_prior_squared_error: 357.9481, root_mean_squared_error: 151.8908, root_relative_squared_error: 0.4243,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set fishcatch
0 likes - 0 downloads - 0 reach - mean_absolute_error: 83.6115, mean_prior_absolute_error: 297.3947, number_of_instances: 158, relative_absolute_error: 0.2811, root_mean_prior_squared_error: 357.9481, root_mean_squared_error: 151.6969, root_relative_squared_error: 0.4238,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set fishcatch
0 likes - 0 downloads - 0 reach - mean_absolute_error: 84.3053, mean_prior_absolute_error: 297.3947, number_of_instances: 158, relative_absolute_error: 0.2835, root_mean_prior_squared_error: 357.9481, root_mean_squared_error: 154.6212, root_relative_squared_error: 0.432,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set fishcatch
0 likes - 0 downloads - 0 reach - mean_absolute_error: 82.5727, mean_prior_absolute_error: 297.3947, number_of_instances: 158, relative_absolute_error: 0.2777, root_mean_prior_squared_error: 357.9481, root_mean_squared_error: 154.0453, root_relative_squared_error: 0.4304,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set fishcatch
0 likes - 0 downloads - 0 reach - mean_absolute_error: 80.1785, mean_prior_absolute_error: 297.3947, number_of_instances: 158, relative_absolute_error: 0.2696, root_mean_prior_squared_error: 357.9481, root_mean_squared_error: 151.8908, root_relative_squared_error: 0.4243,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set fishcatch
0 likes - 0 downloads - 0 reach - mean_absolute_error: 85.5131, mean_prior_absolute_error: 297.3947, number_of_instances: 158, relative_absolute_error: 0.2875, root_mean_prior_squared_error: 357.9481, root_mean_squared_error: 163.2036, root_relative_squared_error: 0.4559,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set fishcatch
0 likes - 0 downloads - 0 reach - mean_absolute_error: 84.3053, mean_prior_absolute_error: 297.3947, number_of_instances: 158, relative_absolute_error: 0.2835, root_mean_prior_squared_error: 357.9481, root_mean_squared_error: 154.6212, root_relative_squared_error: 0.432,

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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 fishcatch (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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