Task
Supervised Regression on hungarian

Supervised Regression on hungarian

Task 2319 Supervised Regression hungarian 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 hungarian
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.2739, mean_prior_absolute_error: 0.4611, number_of_instances: 294, relative_absolute_error: 0.5941, root_mean_prior_squared_error: 0.4802, root_mean_squared_error: 0.3863, root_relative_squared_error: 0.8044,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set hungarian
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.275, mean_prior_absolute_error: 0.4611, number_of_instances: 294, relative_absolute_error: 0.5964, root_mean_prior_squared_error: 0.4802, root_mean_squared_error: 0.3788, root_relative_squared_error: 0.7889,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set hungarian
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.2676, mean_prior_absolute_error: 0.4611, number_of_instances: 294, relative_absolute_error: 0.5804, root_mean_prior_squared_error: 0.4802, root_mean_squared_error: 0.3681, root_relative_squared_error: 0.7667,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set hungarian
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.268, mean_prior_absolute_error: 0.4611, number_of_instances: 294, relative_absolute_error: 0.5812, root_mean_prior_squared_error: 0.4802, root_mean_squared_error: 0.3686, root_relative_squared_error: 0.7678,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set hungarian
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.2619, mean_prior_absolute_error: 0.4611, number_of_instances: 294, relative_absolute_error: 0.5681, root_mean_prior_squared_error: 0.4802, root_mean_squared_error: 0.3689, root_relative_squared_error: 0.7683,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set hungarian
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.2619, mean_prior_absolute_error: 0.4611, number_of_instances: 294, relative_absolute_error: 0.5681, root_mean_prior_squared_error: 0.4802, root_mean_squared_error: 0.3689, root_relative_squared_error: 0.7683,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set hungarian
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.2773, mean_prior_absolute_error: 0.4611, number_of_instances: 294, relative_absolute_error: 0.6014, root_mean_prior_squared_error: 0.4802, root_mean_squared_error: 0.3848, root_relative_squared_error: 0.8014,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set hungarian
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.2795, mean_prior_absolute_error: 0.4611, number_of_instances: 294, relative_absolute_error: 0.6061, root_mean_prior_squared_error: 0.4802, root_mean_squared_error: 0.3818, root_relative_squared_error: 0.7951,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set hungarian
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.268, mean_prior_absolute_error: 0.4611, number_of_instances: 294, relative_absolute_error: 0.5812, root_mean_prior_squared_error: 0.4802, root_mean_squared_error: 0.3686, root_relative_squared_error: 0.7678,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set hungarian
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.2619, mean_prior_absolute_error: 0.4611, number_of_instances: 294, relative_absolute_error: 0.5681, root_mean_prior_squared_error: 0.4802, root_mean_squared_error: 0.3689, root_relative_squared_error: 0.7683,

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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 hungarian (1)
    target_feature num

    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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