sklearn.base.TransformerMixin

class sklearn.base.TransformerMixin

Mixin class for all transformers in scikit-learn.

If get_feature_names_out is defined, then BaseEstimator will automatically wrap transform and fit_transform to follow the set_output API. See the Developer API for set_output for details.

OneToOneFeatureMixin and 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)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

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)

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 and fit_transform.

• "default": Default output format of a transformer

• "pandas": DataFrame output

• None: Transform configuration is unchanged

Returns:

selfestimator instance

Estimator instance.

Examples using sklearn.base.TransformerMixin

Metadata Routing

Metadata Routing

Approximate nearest neighbors in TSNE

Approximate nearest neighbors in TSNE