Task
Supervised Regression on pbc

Supervised Regression on pbc

Task 4850 Supervised Regression pbc 18 runs submitted
1 likes downloaded by 2 people , 3 total downloads 0 issues
Visibility: Public
Issue #Downvotes for this reason By


Metric:

18 Runs

Fetching data
Fetching data
Search runs in more detail
ID's
Sort: most recent
18 results
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.6879, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.7465, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.8491, root_relative_squared_error: 0.8894,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.6408, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.6954, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.7566, root_relative_squared_error: 0.7926,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5777, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.6269, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.7279, root_relative_squared_error: 0.7625,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5773, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.6265, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.7287, root_relative_squared_error: 0.7633,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5808, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.6303, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.8039, root_relative_squared_error: 0.842,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5795, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.6289, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.8032, root_relative_squared_error: 0.8413,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5202, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.5645, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.7024, root_relative_squared_error: 0.7358,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5773, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.6265, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.7287, root_relative_squared_error: 0.7633,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.4995, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.5421, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.7382, root_relative_squared_error: 0.7733,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5795, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.6289, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.8032, root_relative_squared_error: 0.8413,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(2) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5745, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.6234, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.7254, root_relative_squared_error: 0.7598,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(2) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5803, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.6297, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.7307, root_relative_squared_error: 0.7654,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(2) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5778, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.627, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.7282, root_relative_squared_error: 0.7628,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(2) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5778, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.627, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.7282, root_relative_squared_error: 0.7628,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(2) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5808, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.6303, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.8039, root_relative_squared_error: 0.842,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(2) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5809, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.6304, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.7842, root_relative_squared_error: 0.8214,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(2) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5795, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.6289, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.8032, root_relative_squared_error: 0.8413,
Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(2) on task Supervised Regression on data set pbc
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5795, mean_prior_absolute_error: 0.9215, number_of_instances: 418, relative_absolute_error: 0.6289, root_mean_prior_squared_error: 0.9546, root_mean_squared_error: 0.8032, root_relative_squared_error: 0.8413,

    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 pbc (2)
    target_feature status

    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)

    How to submit runs

    Using your favorite machine learning environment

    Download this task directly in your environment and automatically upload your results

    OpenML bootcamp

    From your own software

    Use one of our APIs to download data from OpenML and upload your results

    OpenML APIs