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sklearn.preprocessing.add_dummy_feature(X, value=1.0)
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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}, 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.
Examples
>>> from sklearn.preprocessing import add_dummy_feature >>> add_dummy_feature([[0, 1], [1, 0]]) array([[ 1., 0., 1.], [ 1., 1., 0.]])
sklearn.preprocessing.add_dummy_feature()
2017-01-15 04:26:51
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