19 public 0 Encode categorical features as a one-hot numeric array. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The features are encoded using a one-hot (aka 'one-of-K' or 'dummy') encoding scheme. This creates a binary column for each category and returns a sparse matrix or dense array (depending on the ``sparse`` parameter) By default, the encoder derives the categories based on the unique values in each feature. Alternatively, you can also specify the `categories` manually. This encoding is needed for feeding categorical data to many scikit-learn estimators, notably linear models and SVMs with the standard kernels. Note: a one-hot encoding of y labels should use a LabelBinarizer instead. 2020-01-10T18:00:41Z 0 0 17483 sklearn.preprocessing._encoders.OneHotEncoder sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 sklearn.preprocessing._encoders.OneHotEncoder openml==0.10.2,sklearn==0.22.1