MetaEstimatorMixin (original) (raw)

class sklearn.base.MetaEstimatorMixin[source]#

Mixin class for all meta estimators in scikit-learn.

This mixin is empty, and only exists to indicate that the estimator is a meta-estimator.

Changed in version 1.6: The _required_parameters is now removed and is unnecessary since tests are refactored and don’t use this anymore.

Examples

from sklearn.base import MetaEstimatorMixin from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression class MyEstimator(MetaEstimatorMixin): ... def init(self, *, estimator=None): ... self.estimator = estimator ... def fit(self, X, y=None): ... if self.estimator is None: ... self.estimator_ = LogisticRegression() ... else: ... self.estimator_ = self.estimator ... return self X, y = load_iris(return_X_y=True) estimator = MyEstimator().fit(X, y) estimator.estimator_ LogisticRegression()

Gallery examples#