Correct a typo in documentation
extend -> extent
@@ -75,7 +75,7 @@ class KernelPCA(_ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimato
default='auto'
Select eigensolver to use. If `n_components` is much
less than the number of training samples, randomized (or arpack to a
- smaller extend) may be more efficient than the dense eigensolver.
+ smaller extent) may be more efficient than the dense eigensolver.
Randomized SVD is performed according to the method of Halko et al
[3]_.
