Task
Supervised Regression on cpu_small

Supervised Regression on cpu_small

Task 2315 Supervised Regression cpu_small 5 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 cpu_small
0 likes - 0 downloads - 0 reach - mean_absolute_error: 2.1435, mean_prior_absolute_error: 10.6279, number_of_instances: 8192, relative_absolute_error: 0.2017, root_mean_prior_squared_error: 18.4008, root_mean_squared_error: 2.9583, root_relative_squared_error: 0.1608,
Nico Strasdat 6 years ago
ran flow sklearn.pipeline.Pipeline(miss ... er,predict=hpo4svm.hpo.SVR)(1) on task Supervised Regression on data set cpu_small
0 likes - 0 downloads - 0 reach - mean_absolute_error: 2.1614, mean_prior_absolute_error: 10.6279, number_of_instances: 8192, relative_absolute_error: 0.2034, root_mean_prior_squared_error: 18.4008, root_mean_squared_error: 2.9465, root_relative_squared_error: 0.1601,
Nico Strasdat 6 years ago
ran flow sklearn.pipeline.Pipeline(scal ... hpo4svm.hpo.sklearn.HPOSVM)(1) on task Supervised Regression on data set cpu_small
0 likes - 0 downloads - 0 reach - mean_absolute_error: 2.1755, mean_prior_absolute_error: 10.6279, number_of_instances: 8192, relative_absolute_error: 0.2047, root_mean_prior_squared_error: 18.4008, root_mean_squared_error: 2.9648, root_relative_squared_error: 0.1611,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart.dummied(1) on task Supervised Regression on data set cpu_small
0 likes - 0 downloads - 0 reach - mean_absolute_error: 3.9335, mean_prior_absolute_error: 10.6279, number_of_instances: 8192, relative_absolute_error: 0.3701, root_mean_prior_squared_error: 18.4008, root_mean_squared_error: 5.1913, root_relative_squared_error: 0.2821, usercpu_time_millis: 0.481, usercpu_time_millis_testing: 0.024, usercpu_time_millis_training: 0.457,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart(5) on task Supervised Regression on data set cpu_small
0 likes - 0 downloads - 0 reach - mean_absolute_error: 3.9335, mean_prior_absolute_error: 10.6279, number_of_instances: 8192, relative_absolute_error: 0.3701, root_mean_prior_squared_error: 18.4008, root_mean_squared_error: 5.1913, root_relative_squared_error: 0.2821, usercpu_time_millis: 0.255, usercpu_time_millis_testing: 0.019, usercpu_time_millis_training: 0.236,

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    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 cpu_small (1)
    target_feature usr

    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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