sklearn.base.clone(estimator, *, safe=True)[source]#

Construct a new unfitted estimator with the same parameters.

Clone does a deep copy of the model in an estimator without actually copying attached data. It returns a new estimator with the same parameters that has not been fitted on any data.

Changed in version 1.3: Delegates to estimator.__sklearn_clone__ if the method exists.

estimator{list, tuple, set} of estimator instance or a single estimator instance

The estimator or group of estimators to be cloned.

safebool, default=True

If safe is False, clone will fall back to a deep copy on objects that are not estimators. Ignored if estimator.__sklearn_clone__ exists.


The deep copy of the input, an estimator if input is an estimator.


If the estimator’s random_state parameter is an integer (or if the estimator doesn’t have a random_state parameter), an exact clone is returned: the clone and the original estimator will give the exact same results. Otherwise, statistical clone is returned: the clone might return different results from the original estimator. More details can be found in Controlling randomness.


>>> from sklearn.base import clone
>>> from sklearn.linear_model import LogisticRegression
>>> X = [[-1, 0], [0, 1], [0, -1], [1, 0]]
>>> y = [0, 0, 1, 1]
>>> classifier = LogisticRegression().fit(X, y)
>>> cloned_classifier = clone(classifier)
>>> hasattr(classifier, "classes_")
>>> hasattr(cloned_classifier, "classes_")
>>> classifier is cloned_classifier