sklearn.preprocessing.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:

Parameters:	X : {array-like, sparse matrix}, shape [n_samples, n_features] Data. value : float Value to use for the dummy feature.
Returns:	X : {array, sparse matrix}, shape [n_samples, n_features + 1] Same data with dummy feature added as first column.

X : {array-like, sparse matrix}, shape [n_samples, n_features]

Data.

value : float

Value to use for the dummy feature.

Returns:

X : {array, sparse matrix}, shape [n_samples, n_features + 1]

Same data with dummy feature added as first column.

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

Links:

doc_scikit_learn

2025-01-10 15:47:30

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