pycaret.classification.finalize_model

pycaret.classification.finalize_model(estimator, fit_kwargs: dict | None = None, groups: str | Any | None = None, model_only: bool = False, experiment_custom_tags: Dict[str, Any] | None = None) → Any

This function trains a given estimator on the entire dataset including the holdout set.

Example

>>> from pycaret.datasets import get_data
>>> juice = get_data('juice')
>>> from pycaret.classification import *
>>> exp_name = setup(data = juice,  target = 'Purchase')
>>> lr = create_model('lr')
>>> final_lr = finalize_model(lr)

estimator: scikit-learn compatible object

Trained model object

fit_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the fit method of the model.

groups: str or array-like, with shape (n_samples,), default = None

Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.

model_onlybool, default = False

Whether to return the complete fitted pipeline or only the fitted model.

experiment_custom_tags: dict, default = None

Dictionary of tag_name: String -> value: (String, but will be string-ified if not) passed to the mlflow.set_tags to add new custom tags for the experiment.

Returns:

Trained pipeline or model object fitted on complete dataset.