18913 6691 sklearn.preprocessing.data.OneHotEncoder sklearn.OneHotEncoder sklearn.preprocessing.data.OneHotEncoder 30 openml==0.12.2,sklearn==0.18.1 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. 2021-08-13T19:24:49 English sklearn==0.18.1 numpy>=1.6.1 scipy>=0.9 categorical_features [3, 4, 7, 8] dtype number type {"oml-python:serialized_object": "type", "value": "np.float64"} Desired dtype of output handle_unknown str "ignore" Whether to raise an error or ignore if a unknown categorical feature is present during transform. n_values 'auto' "auto" Number of values per feature - 'auto' : determine value range from training data sparse boolean false Will return sparse matrix if set True else will return an array openml-python python scikit-learn sklearn sklearn_0.18.1