pycaret.regression.load_model

pycaret.regression.load_model(model_name: str, platform: str | None = None, authentication: Dict[str, str] | None = None, verbose: bool = True)

This function loads a previously saved pipeline.

Example

>>> from pycaret.regression import load_model
>>> saved_lr = load_model('saved_lr_model')

model_name: str

Name of the model.

platform: str, default = None

Name of the cloud platform. Currently supported platforms: ‘aws’, ‘gcp’ and ‘azure’.

authentication: dict, default = None

dictionary of applicable authentication tokens.

when platform = ‘aws’: {‘bucket’ : ‘Name of Bucket on S3’, ‘path’: (optional) folder name under the bucket}

when platform = ‘gcp’: {‘project’: ‘gcp-project-name’, ‘bucket’ : ‘gcp-bucket-name’}

when platform = ‘azure’: {‘container’: ‘azure-container-name’}

verbose: bool, default = True

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

Returns:

Trained Model