Task
Supervised Regression on analcatdata_gsssexsurvey

Supervised Regression on analcatdata_gsssexsurvey

Task 4834 Supervised Regression analcatdata_gsssexsurvey 18 runs submitted
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Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5253, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 0.9916, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7591, root_relative_squared_error: 1.0109,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5332, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 1.0066, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7989, root_relative_squared_error: 1.0638,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5332, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 1.0066, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7989, root_relative_squared_error: 1.0638,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5258, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 0.9926, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7593, root_relative_squared_error: 1.0112,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5259, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 0.9927, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7594, root_relative_squared_error: 1.0112,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5332, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 1.0066, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7989, root_relative_squared_error: 1.0638,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5409, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 1.0212, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7881, root_relative_squared_error: 1.0495,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5304, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 1.0013, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7581, root_relative_squared_error: 1.0095,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5259, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 0.9927, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7594, root_relative_squared_error: 1.0112,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5332, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 1.0066, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7989, root_relative_squared_error: 1.0638,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(2) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5258, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 0.9926, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7593, root_relative_squared_error: 1.0112,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(2) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5125, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 0.9676, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7581, root_relative_squared_error: 1.0095,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(2) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5259, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 0.9927, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7594, root_relative_squared_error: 1.0112,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(2) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5259, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 0.9927, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7594, root_relative_squared_error: 1.0112,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(2) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5332, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 1.0066, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7989, root_relative_squared_error: 1.0638,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(2) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5265, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 0.9938, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7897, root_relative_squared_error: 1.0517,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(2) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5332, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 1.0066, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7989, root_relative_squared_error: 1.0638,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(2) on task Supervised Regression on data set analcatdata_gsssexsurvey
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5332, mean_prior_absolute_error: 0.5297, number_of_instances: 159, relative_absolute_error: 1.0066, root_mean_prior_squared_error: 0.7509, root_mean_squared_error: 0.7989, root_relative_squared_error: 1.0638,

    Metric:

    Timeline

    Plotting contribution timeline

    Leaderboard

    Rank Name Top Score Entries Highest rank

    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 analcatdata_gsssexsurvey (1)
    target_feature AIDS_know

    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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