pycaret.regression.save_model

pycaret.regression.save_model(model, model_name: str, model_only: bool = False, verbose: bool = True, **kwargs)

This function saves the transformation pipeline and trained model object into the current working directory as a pickle file for later use.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> save_model(lr, 'saved_lr_model')

model: scikit-learn compatible object

Trained model object

model_name: str

Name of the model.

model_only: bool, default = False

When set to True, only trained model object is saved instead of the entire pipeline.

**kwargs:

Additional keyword arguments to pass to joblib.dump().

verbose: bool, default = True

Success message is not printed when verbose is set to False.

Returns:

Tuple of the model object and the filename.