public 27 openml==0.12.2,sklearn==0.22.2.post1 0 0 18850 sklearn==0.22.2.post1 numpy>=1.11.0 scipy>=0.17.0 joblib>=0.11 0 2021-01-06T12:15:55Z 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. sklearn.preprocessing._encoders.OneHotEncoder sklearn.preprocessing._encoders.OneHotEncoder