Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the What you really need to know section for the big picture.

julearn.run_fit

julearn.run_fit(X, y, model, data, X_types=None, problem_type=None, preprocess=None, groups=None, pos_labels=None, model_params=None, search_params=None, seed=None, verbose=0)

Fit the model on all the data.

Parameters:

  • X (list[str]) – The features to use. See Data for details.

  • y (str) – The targets to predict. See Data for details.

  • model (str | PipelineCreator | BaseEstimator | list[PipelineCreator]) – If string, it will use one of the available models.

  • data (DataFrame) – DataFrame with the data. See Data for details.

  • X_types (dict | None, default: None) – A dictionary containing keys with column type as a str and the columns of this column type as a list of str.

  • problem_type – The kind of problem to model.

Returns:

  • scores (pd.DataFrame) – The resulting scores (one column for each score specified). Additionally, a ‘fit_time’ column will be added. And, if return_estimator='all' or return_estimator='cv', an ‘estimator’ columns with the corresponding estimators fitted for each CV split.

  • final_estimator (object) – The final estimator, fitted on all the data (only if return_estimator='all' or return_estimator='final')

  • inspector (Inspector | None) – The inspector object (only if return_inspector=True)