sklearn.preprocessing.data.OneHotEncoder 18806 0 Encode categorical integer features using a one-hot aka one-of-K scheme. The input to this transformer should be a matrix of integers, denoting the values taken on by categorical (discrete) features. The output will be a sparse matrix where each column corresponds to one possible value of one feature. It is assumed that input features take on values in the range [0, n_values). 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. 29 0 2021-01-13T22:07:00Z sklearn.preprocessing.data.OneHotEncoder sklearn==0.18.1 numpy>=1.6.1 scipy>=0.9 0 openml==0.11.0,sklearn==0.18.1 public