pycaret.classification.calibrate_model#

pycaret.classification.calibrate_model(estimator, method: str = 'sigmoid', calibrate_fold: int | Any | None = 5, fold: int | Any | None = None, round: int = 4, fit_kwargs: dict | None = None, groups: str | Any | None = None, verbose: bool = True, return_train_score: bool = False) → Any[source]#

This function calibrates the probability of a given estimator using isotonic or logistic regression. 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. The output of the original estimator and the calibrated estimator (created using this function) might not differ much. In order to see the calibration differences, use ‘calibration’ plot in plot_model to see the difference before and after.

Example

>>> from pycaret.datasets import get_data
>>> juice = get_data('juice')
>>> from pycaret.classification import *
>>> exp_name = setup(data = juice,  target = 'Purchase')
>>> dt = create_model('dt')
>>> calibrated_dt = calibrate_model(dt)
estimator: scikit-learn compatible object

Trained model object

method: str, default = ‘sigmoid’

The method to use for calibration. Can be ‘sigmoid’ which corresponds to Platt’s method or ‘isotonic’ which is a non-parametric approach.

calibrate_fold: integer or scikit-learn compatible CV generator, default = 5

Controls internal cross-validation. Can be an integer or a scikit-learn CV generator. If set to an integer, will use (Stratifed)KFold CV with that many folds. See scikit-learn documentation on Stacking for more details.

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.

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

Warning

  • Avoid isotonic calibration with too few calibration samples (< 1000) since it tends to overfit.

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