cardea.modeling.Modeler.evaluate

Modeler.evaluate(X, y, test_size=0.2, shuffle=True, tune=False, max_evals=10, scoring=None, metrics=None, verbose=False)[source]

Evaluate the pipelines.

Parameters

  • X (pandas.DataFrame or ndarray) – Inputs to the pipeline.

  • y (pandas.Series or ndarray) – Target values.

  • test_size (float) – The proportion of the dataset to include in the test dataset.

  • shuffle (bool) – Whether or not to shuffle the data before splitting.

  • tune (bool) – Whether to optimize hyper-parameters of the pipelines.

  • max_evals (int) – Maximum number of hyper-parameter optimization iterations.

  • scoring (str) – The name of the scoring function used in the hyper-parameter optimization.

  • metrics (list) – A list of scoring function names. The scoring functions should be consistent with the problem type.

  • verbose (bool) – Whether to log information during processing.

cardea.modeling.Modeler.fit_predict cardea.modeling.Modeler.save