pycaret.regression.blend_models#

pycaret.regression.blend_models(estimator_list: list, fold: int | Any | None = None, round: int = 4, choose_better: bool = False, optimize: str = 'R2', weights: List[float] | None = None, fit_kwargs: dict | None = None, groups: str | Any | None = None, verbose: bool = True, return_train_score: bool = False)[source]#

This function trains a Voting Regressor for select models passed in the estimator_list param. The output of this function is a score grid with CV scores by fold. Metrics evaluated during CV can be accessed using the get_metrics function. Custom metrics can be added or removed using add_metric and remove_metric 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)
>>> blender = blend_models(top3)

estimator_list: list of scikit-learn compatible objects: List of trained model objects
fold: int or scikit-learn compatible CV generator, default = None: Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.
round: int, default = 4: Number of decimal places the metrics in the score grid will be rounded to.
choose_better: bool, default = False: When set to True, the returned object is always better performing. The metric used for comparison is defined by the optimize parameter.
optimize: str, default = ‘R2’: Metric to compare for model selection when choose_better is True.
weights: list, default = None: Sequence of weights (float or int) to weight the occurrences of predicted class labels (hard voting) or class probabilities before averaging (soft voting). Uses uniform weights when None.
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.
verbose: bool, default = True: Score grid is not printed when verbose is set to False.
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