0 0 openml==0.10.1dev,sklearn==0.21.3 0 sklearn==0.21.3 numpy>=1.6.1 scipy>=0.9 sklearn.preprocessing._encoders.OneHotEncoder 18 17450 2019-01-03T20:37:14Z sklearn.preprocessing._encoders.OneHotEncoder public Encode categorical integer 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. By default, the encoder derives the categories based on the unique values in each feature. Alternatively, you can also specify the `categories` manually. The OneHotEncoder previously assumed that the input features take on values in the range [0, max(values)). This behaviour is deprecated. 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.