pycaret.regression.automl#

pycaret.regression.automl(optimize: str = 'R2', use_holdout: bool = False, turbo: bool = True, return_train_score: bool = False) → Any[source]#

This function returns the best model out of all trained models in current session based on the optimize parameter. Metrics evaluated can be accessed using the get_metrics function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> top3 = compare_models(n_select = 3)
>>> tuned_top3 = [tune_model(i) for i in top3]
>>> blender = blend_models(tuned_top3)
>>> stacker = stack_models(tuned_top3)
>>> best_mae_model = automl(optimize = 'MAE')

optimize: str, default = ‘R2’: Metric to use for model selection. It also accepts custom metrics added using the add_metric function.
use_holdout: bool, default = False: When set to True, metrics are evaluated on holdout set instead of CV.
turbo: bool, default = True: When set to True and use_holdout is False, only models created with default fold parameter will be considered. If set to False, models created with a non-default fold parameter will be scored again using default fold settings, so that they can be compared.
return_train_score: bool, default = False: If False, returns the CV Validation scores only. If True, returns the CV training scores along with the CV validation scores. This is useful when the user wants to do bias-variance tradeoff. A high CV training score with a low corresponding CV validation score indicates overfitting.

Returns:: Trained Model