sklearn.base
.TransformerMixin¶
- class sklearn.base.TransformerMixin[source]¶
Mixin class for all transformers in scikit-learn.
If get_feature_names_out is defined, then
BaseEstimator
will automatically wraptransform
andfit_transform
to follow theset_output
API. See the Developer API for set_output for details.base.OneToOneFeatureMixin
andbase.ClassNamePrefixFeaturesOutMixin
are helpful mixins for defining get_feature_names_out.Methods
fit_transform
(X[, y])Fit to data, then transform it.
set_output
(*[, transform])Set output container.
- fit_transform(X, y=None, **fit_params)[source]¶
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
.- Parameters:
- Xarray-like of shape (n_samples, n_features)
Input samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
- **fit_paramsdict
Additional fit parameters.
- Returns:
- X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
- set_output(*, transform=None)[source]¶
Set output container.
See Introducing the set_output API for an example on how to use the API.
- Parameters:
- transform{“default”, “pandas”}, default=None
Configure output of
transform
andfit_transform
."default"
: Default output format of a transformer"pandas"
: DataFrame outputNone
: Transform configuration is unchanged
- Returns:
- selfestimator instance
Estimator instance.
Examples using sklearn.base.TransformerMixin
¶
Approximate nearest neighbors in TSNE