Task
Supervised Regression on cristalli

Supervised Regression on cristalli

Task 4794 Supervised Regression cristalli 5 runs submitted
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Tan Zheng 4 years ago
ran flow sklearn.ensemble._forest.RandomForestRegressor(1) on task Supervised Regression on data set cristalli
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 0.1809, number_of_instances: 32, root_mean_prior_squared_error: 0.2705,
Tan Zheng 4 years ago
ran flow sklearn.neighbors._regression.KNeighborsRegressor(2) on task Supervised Regression on data set cristalli
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 0.1809, number_of_instances: 32, root_mean_prior_squared_error: 0.2705,
Tan Zheng 4 years ago
ran flow sklearn.svm._classes.SVR(1) on task Supervised Regression on data set cristalli
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 0.1809, number_of_instances: 32, root_mean_prior_squared_error: 0.2705,
Tan Zheng 4 years ago
ran flow sklearn.tree._classes.DecisionTreeRegressor(1) on task Supervised Regression on data set cristalli
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 0.1809, number_of_instances: 32, root_mean_prior_squared_error: 0.2705,
Tan Zheng 4 years ago
ran flow sklearn.linear_model._base.LinearRegression(2) on task Supervised Regression on data set cristalli
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 0.1809, number_of_instances: 32, root_mean_prior_squared_error: 0.2705,

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    Note: The leaderboard ignores resubmissions of previous solutions, as well as parameter variations that do not improve performance.

    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 cristalli (1)
    target_feature oz1143

    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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