pycaret.regression.add_metric

pycaret.regression.add_metric(id: str, name: str, score_func: type, greater_is_better: bool = True, **kwargs) → Series

Adds a custom metric to be used in the experiment.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> from sklearn.metrics import explained_variance_score
>>> add_metric('evs', 'EVS', explained_variance_score)

id: str

Unique id for the metric.

name: str

Display name of the metric.

score_func: type

Score function (or loss function) with signature score_func(y, y_pred, **kwargs).

greater_is_better: bool, default = True

Whether score_func is higher the better or not.

**kwargs:

Arguments to be passed to score function.

Returns:

pandas.Series