dtoolkit.pipeline.FeatureUnion.fit_transform

FeatureUnion.fit_transform(X, y=None, **fit_params)

Fit all transformers, transform the data and concatenate results.

Parameters:

Xiterable or array-like, depending on transformers

Input data to be transformed.

yarray-like of shape (n_samples, n_outputs), default=None

Targets for supervised learning.

**fit_paramsdict, default=None

Parameters to pass to the fit method of the estimator.

Returns:

X_tarray-like or sparse matrix of shape (n_samples, sum_n_components)

The hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers.