sklearn.utils.validation.check_is_fitted

sklearn.utils.validation.check_is_fitted(estimator, attributes=None, *, msg=None, all_or_any=<built-in function all>)[source]

Perform is_fitted validation for estimator.

Checks if the estimator is fitted by verifying the presence of fitted attributes (ending with a trailing underscore) and otherwise raises a NotFittedError with the given message.

If an estimator does not set any attributes with a trailing underscore, it can define a __sklearn_is_fitted__ method returning a boolean to specify if the estimator is fitted or not.

Parameters:
estimatorestimator instance

estimator instance for which the check is performed.

attributesstr, list or tuple of str, default=None

Attribute name(s) given as string or a list/tuple of strings Eg.: ["coef_", "estimator_", ...], "coef_"

If None, estimator is considered fitted if there exist an attribute that ends with a underscore and does not start with double underscore.

msgstr, default=None

The default error message is, “This %(name)s instance is not fitted yet. Call ‘fit’ with appropriate arguments before using this estimator.”

For custom messages if “%(name)s” is present in the message string, it is substituted for the estimator name.

Eg. : “Estimator, %(name)s, must be fitted before sparsifying”.

all_or_anycallable, {all, any}, default=all

Specify whether all or any of the given attributes must exist.

Returns:
None
Raises:
NotFittedError

If the attributes are not found.

Examples using sklearn.utils.validation.check_is_fitted