pycaret.regression.evaluate_model

pycaret.regression.evaluate_model(estimator, fold: int | Any | None = None, fit_kwargs: dict | None = None, plot_kwargs: dict | None = None, groups: str | Any | None = None)

This function displays a user interface for analyzing performance of a trained model. It calls the plot_model function internally.

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')
>>> evaluate_model(lr)

estimator: scikit-learn compatible object

Trained model object

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.

fit_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the fit method of the model.

plot_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the visualizer class.

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.

Returns:

None

Warning

• This function only works in IPython enabled Notebook.