Task
Supervised Regression on FacultySalaries

Supervised Regression on FacultySalaries

Task 5037 Supervised Regression FacultySalaries 3 runs submitted
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Nico Strasdat 6 years ago
ran flow sklearn.pipeline.Pipeline(scal ... hpo4svm.hpo.sklearn.HPOSVM)(1) on task Supervised Regression on data set FacultySalaries
0 likes - 0 downloads - 0 reach - mean_absolute_error: 1.6536, mean_prior_absolute_error: 3.4219, number_of_instances: 50, relative_absolute_error: 0.4832, root_mean_prior_squared_error: 4.5713, root_mean_squared_error: 2.7706, root_relative_squared_error: 0.6061,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart.dummied(1) on task Supervised Regression on data set FacultySalaries
0 likes - 0 downloads - 0 reach - mean_absolute_error: 2.0195, mean_prior_absolute_error: 3.4219, number_of_instances: 50, relative_absolute_error: 0.5902, root_mean_prior_squared_error: 4.5713, root_mean_squared_error: 2.7689, root_relative_squared_error: 0.6057, usercpu_time_millis: 0.112, usercpu_time_millis_testing: 0.021, usercpu_time_millis_training: 0.091,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart(5) on task Supervised Regression on data set FacultySalaries
0 likes - 0 downloads - 0 reach - mean_absolute_error: 2.0195, mean_prior_absolute_error: 3.4219, number_of_instances: 50, relative_absolute_error: 0.5902, root_mean_prior_squared_error: 4.5713, root_mean_squared_error: 2.7689, root_relative_squared_error: 0.6057, usercpu_time_millis: 0.048, usercpu_time_millis_testing: 0.012, usercpu_time_millis_training: 0.036,

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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 FacultySalaries (1)
    target_feature asst.prof.salary

    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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