add_dummy_feature

sklearn.preprocessing.add_dummy_feature(X, value=1.0)[source]

Augment dataset with an additional dummy feature.

This is useful for fitting an intercept term with implementations which cannot otherwise fit it directly.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

Data.

valuefloat

Value to use for the dummy feature.

Returns:

X{ndarray, sparse matrix} of shape (n_samples, n_features + 1)

Same data with dummy feature added as first column.

Examples

>>> from sklearn.preprocessing import add_dummy_feature
>>> add_dummy_feature([[0, 1], [1, 0]])
array([[1., 0., 1.],
       [1., 1., 0.]])