pycaret.classification.blend_models#

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

This function trains a Soft Voting / Majority Rule classifier 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
>>> juice = get_data('juice')
>>> from pycaret.classification import *
>>> exp_name = setup(data = juice,  target = 'Purchase')
>>> 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 = ‘Accuracy’

Metric to compare for model selection when choose_better is True.

method: str, default = ‘auto’

‘hard’ uses predicted class labels for majority rule voting. ‘soft’, predicts the class label based on the argmax of the sums of the predicted probabilities, which is recommended for an ensemble of well-calibrated classifiers. Default value, ‘auto’, will try to use ‘soft’ and fall back to ‘hard’ if the former is not supported.

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.

probability_threshold: float, default = None

Threshold for converting predicted probability to class label. It defaults to 0.5 for all classifiers unless explicitly defined in this parameter. Only applicable for binary classification.

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

On this page