Task
Supervised Regression on stock

Supervised Regression on stock

Task 2311 Supervised Regression stock 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 stock
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.4969, mean_prior_absolute_error: 5.4758, number_of_instances: 950, relative_absolute_error: 0.0908, root_mean_prior_squared_error: 6.536, root_mean_squared_error: 0.6594, root_relative_squared_error: 0.1009,
Nico Strasdat 6 years ago
ran flow sklearn.pipeline.Pipeline(miss ... er,predict=hpo4svm.hpo.SVR)(1) on task Supervised Regression on data set stock
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.4891, mean_prior_absolute_error: 5.4758, number_of_instances: 950, relative_absolute_error: 0.0893, root_mean_prior_squared_error: 6.536, root_mean_squared_error: 0.6458, root_relative_squared_error: 0.0988,
Nico Strasdat 6 years ago
ran flow sklearn.pipeline.Pipeline(scal ... hpo4svm.hpo.sklearn.HPOSVM)(1) on task Supervised Regression on data set stock
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.4751, mean_prior_absolute_error: 5.4758, number_of_instances: 950, relative_absolute_error: 0.0868, root_mean_prior_squared_error: 6.536, root_mean_squared_error: 0.6253, root_relative_squared_error: 0.0957,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart.dummied(1) on task Supervised Regression on data set stock
0 likes - 0 downloads - 0 reach - mean_absolute_error: 1.5904, mean_prior_absolute_error: 5.4758, number_of_instances: 950, relative_absolute_error: 0.2904, root_mean_prior_squared_error: 6.536, root_mean_squared_error: 1.9695, root_relative_squared_error: 0.3013, usercpu_time_millis: 0.1, usercpu_time_millis_testing: 0.018, usercpu_time_millis_training: 0.082,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart(5) on task Supervised Regression on data set stock
0 likes - 0 downloads - 0 reach - mean_absolute_error: 1.5904, mean_prior_absolute_error: 5.4758, number_of_instances: 950, relative_absolute_error: 0.2904, root_mean_prior_squared_error: 6.536, root_mean_squared_error: 1.9695, root_relative_squared_error: 0.3013, usercpu_time_millis: 0.067, usercpu_time_millis_testing: 0.016, usercpu_time_millis_training: 0.051,

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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 stock (1)
    target_feature company10

    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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