Task
Supervised Regression on Concrete_Data

Supervised Regression on Concrete_Data

Task 190423 Supervised Regression Concrete_Data 3 runs submitted
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Christian Geißler 5 years ago
ran flow sklearn.pipeline.Pipeline(impu ... stic_gradient.SGDRegressor)(1) on task Supervised Regression on data set Concrete_Data
0 likes - 0 downloads - 0 reach - mean_absolute_error: 3074530572.7152, mean_prior_absolute_error: 13.4607, number_of_instances: 1030, relative_absolute_error: 228408633.6746, root_mean_prior_squared_error: 16.6976, root_mean_squared_error: 5414758779.7652, root_relative_squared_error: 324283065.7459,
Christian Geißler 5 years ago
ran flow sklearn.pipeline.Pipeline(impu ... stic_gradient.SGDRegressor)(1) on task Supervised Regression on data set Concrete_Data
0 likes - 0 downloads - 0 reach - mean_absolute_error: 13052417797042, mean_prior_absolute_error: 13.4607, number_of_instances: 1030, relative_absolute_error: 969671579014.22, root_mean_prior_squared_error: 16.6976, root_mean_squared_error: 14697181975778, root_relative_squared_error: 880195669424.93,
Christian Geißler 5 years ago
ran flow sklearn.pipeline.Pipeline(impu ... stic_gradient.SGDRegressor)(1) on task Supervised Regression on data set Concrete_Data
0 likes - 0 downloads - 0 reach - mean_absolute_error: 1.0223427247858E+16, mean_prior_absolute_error: 13.4607, number_of_instances: 1030, relative_absolute_error: 7.5950425404052E+14, root_mean_prior_squared_error: 16.6976, root_mean_squared_error: 1.1396894873002E+16, root_relative_squared_error: 6.8254564233062E+14,

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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 root_mean_squared_error
    source_data Concrete_Data (1)
    target_feature Concrete compressive strength(MPa. megapascals)

    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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