FrozenEstimator

classsklearn.frozen.FrozenEstimator(estimator)[source]

Estimator that wraps a fitted estimator to prevent re-fitting.

This meta-estimator takes an estimator and freezes it, in the sense that calling fit on it has no effect. fit_predict and fit_transform are also disabled. All other methods are delegated to the original estimator and original estimator’s attributes are accessible as well.

This is particularly useful when you have a fitted or a pre-trained model as a transformer in a pipeline, and you’d like pipeline.fit to have no effect on this step.

Parameters:

estimatorestimator

The estimator which is to be kept frozen.

See also

None

No similar entry in the scikit-learn documentation.

Examples

>>> from sklearn.datasets import make_classification
>>> from sklearn.frozen import FrozenEstimator
>>> from sklearn.linear_model import LogisticRegression
>>> X, y = make_classification(random_state=0)
>>> clf = LogisticRegression(random_state=0).fit(X, y)
>>> frozen_clf = FrozenEstimator(clf)
>>> frozen_clf.fit(X, y)  # No-op
FrozenEstimator(estimator=LogisticRegression(random_state=0))
>>> frozen_clf.predict(X)  # Predictions from `clf.predict`
array(...)

fit(X, y, *args, **kwargs)[source]

No-op.

As a frozen estimator, calling fit has no effect.

Parameters:

Xobject

Ignored.

yobject

Ignored.

*argstuple

Additional positional arguments. Ignored, but present for API compatibility with self.estimator.

**kwargsdict

Additional keyword arguments. Ignored, but present for API compatibility with self.estimator.

Returns:

selfobject

Returns the instance itself.

get_metadata_routing()[source]

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]

Get parameters for this estimator.

Returns a {"estimator": estimator} dict. The parameters of the inner estimator are not included.

Parameters:

deepbool, default=True

Ignored.

Returns:

paramsdict

Parameter names mapped to their values.

set_params(**kwargs)[source]

Set the parameters of this estimator.

The only valid key here is estimator. You cannot set the parameters of the inner estimator.

Parameters:

**kwargsdict

Estimator parameters.

Returns:

selfFrozenEstimator

This estimator.

Gallery examples

Release Highlights for scikit-learn 1.6

Probability Calibration for 3-class classification

Examples of Using FrozenEstimator

Post-tuning the decision threshold for cost-sensitive learning