Task
Supervised Regression on pyrim

Supervised Regression on pyrim

Task 2308 Supervised Regression pyrim 6 runs submitted
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Nico Strasdat 6 years ago
ran flow sklearn.pipeline.Pipeline(miss ... er,predict=hpo4svm.hpo.SVR)(1) on task Supervised Regression on data set pyrim
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.0559, mean_prior_absolute_error: 0.0963, number_of_instances: 74, relative_absolute_error: 0.5808, root_mean_prior_squared_error: 0.1274, root_mean_squared_error: 0.104, root_relative_squared_error: 0.8165,
Nico Strasdat 6 years ago
ran flow sklearn.pipeline.Pipeline(miss ... er,predict=hpo4svm.hpo.SVR)(1) on task Supervised Regression on data set pyrim
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.0515, mean_prior_absolute_error: 0.0963, number_of_instances: 74, relative_absolute_error: 0.5349, root_mean_prior_squared_error: 0.1274, root_mean_squared_error: 0.0846, root_relative_squared_error: 0.664,
Nico Strasdat 6 years ago
ran flow sklearn.pipeline.Pipeline(scal ... er,predict=hpo4svm.hpo.SVR)(1) on task Supervised Regression on data set pyrim
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.0562, mean_prior_absolute_error: 0.0963, number_of_instances: 74, relative_absolute_error: 0.5837, root_mean_prior_squared_error: 0.1274, root_mean_squared_error: 0.1042, root_relative_squared_error: 0.8182,
Nico Strasdat 6 years ago
ran flow sklearn.pipeline.Pipeline(miss ... er,predict=hpo4svm.hpo.SVR)(1) on task Supervised Regression on data set pyrim
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.0555, mean_prior_absolute_error: 0.0963, number_of_instances: 74, relative_absolute_error: 0.5765, root_mean_prior_squared_error: 0.1274, root_mean_squared_error: 0.1018, root_relative_squared_error: 0.7987,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart.dummied(1) on task Supervised Regression on data set pyrim
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.0683, mean_prior_absolute_error: 0.0963, number_of_instances: 74, relative_absolute_error: 0.7092, root_mean_prior_squared_error: 0.1274, root_mean_squared_error: 0.1133, root_relative_squared_error: 0.8893, usercpu_time_millis: 0.124, usercpu_time_millis_testing: 0.028, usercpu_time_millis_training: 0.096,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart(5) on task Supervised Regression on data set pyrim
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.0683, mean_prior_absolute_error: 0.0963, number_of_instances: 74, relative_absolute_error: 0.7092, root_mean_prior_squared_error: 0.1274, root_mean_squared_error: 0.1133, root_relative_squared_error: 0.8893, usercpu_time_millis: 0.076, usercpu_time_millis_testing: 0.029, usercpu_time_millis_training: 0.047,

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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 pyrim (1)
    target_feature activity

    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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