Task
Supervised Regression on triazines

Supervised Regression on triazines

Task 2297 Supervised Regression triazines 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 triazines
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.1139, mean_prior_absolute_error: 0.1181, number_of_instances: 186, relative_absolute_error: 0.9646, root_mean_prior_squared_error: 0.1573, root_mean_squared_error: 0.1538, root_relative_squared_error: 0.978,
Nico Strasdat 6 years ago
ran flow sklearn.pipeline.Pipeline(miss ... er,predict=hpo4svm.hpo.SVR)(1) on task Supervised Regression on data set triazines
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.1055, mean_prior_absolute_error: 0.1181, number_of_instances: 186, relative_absolute_error: 0.8934, root_mean_prior_squared_error: 0.1573, root_mean_squared_error: 0.1453, root_relative_squared_error: 0.9236,
Nico Strasdat 6 years ago
ran flow sklearn.pipeline.Pipeline(scal ... hpo4svm.hpo.sklearn.HPOSVM)(1) on task Supervised Regression on data set triazines
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.1053, mean_prior_absolute_error: 0.1181, number_of_instances: 186, relative_absolute_error: 0.8919, root_mean_prior_squared_error: 0.1573, root_mean_squared_error: 0.1451, root_relative_squared_error: 0.9224,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart.dummied(1) on task Supervised Regression on data set triazines
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.1041, mean_prior_absolute_error: 0.1181, number_of_instances: 186, relative_absolute_error: 0.8814, root_mean_prior_squared_error: 0.1573, root_mean_squared_error: 0.1448, root_relative_squared_error: 0.9207, usercpu_time_millis: 0.173, usercpu_time_millis_testing: 0.044, usercpu_time_millis_training: 0.129,
Heidi Seibold 7 years ago
ran flow mlr.regr.rpart(5) on task Supervised Regression on data set triazines
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.1041, mean_prior_absolute_error: 0.1181, number_of_instances: 186, relative_absolute_error: 0.8814, root_mean_prior_squared_error: 0.1573, root_mean_squared_error: 0.1448, root_relative_squared_error: 0.9207, usercpu_time_millis: 0.165, usercpu_time_millis_testing: 0.045, usercpu_time_millis_training: 0.12,

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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 triazines (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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