17426 1 sklearn.preprocessing._encoders.OneHotEncoder sklearn.OneHotEncoder sklearn.preprocessing._encoders.OneHotEncoder 17 openml==0.10.2,sklearn==0.21.3 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. 2019-11-22T02:17:02 English sklearn==0.21.3 numpy>=1.6.1 scipy>=0.9 categorical_features 'all' or array of indices or mask null Specify what features are treated as categorical - 'all': All features are treated as categorical - array of indices: Array of categorical feature indices - mask: Array of length n_features and with dtype=bool Non-categorical features are always stacked to the right of the matrix .. deprecated:: 0.20 The `categorical_features` keyword was deprecated in version 0.20 and will be removed in 0.22 You can use the ``ColumnTransformer`` instead. categories 'auto' or a list of lists null Categories (unique values) per feature: - 'auto' : Determine categories automatically from the training data - list : ``categories[i]`` holds the categories expected in the ith column. The passed categories should not mix strings and numeric values within a single feature, and should be sorted in case of numeric values The used categories can be found in the ``categories_`` attribute drop 'first' or a list null Specifies a methodology to use to drop one of the categories per feature. This is useful in situations where perfectly collinear features cause problems, such as when feeding the resulting data into a neural network or an unregularized regression dtype number type {"oml-python:serialized_object": "type", "value": "np.float64"} Desired dtype of output handle_unknown 'error' or "ignore" Whether to raise an error or ignore if an unknown categorical feature is present during transform (default is to raise). When this parameter is set to 'ignore' and an unknown category is encountered during transform, the resulting one-hot encoded columns for this feature will be all zeros. In the inverse transform, an unknown category will be denoted as None n_values 'auto' null Number of values per feature - 'auto' : determine value range from training data sparse boolean true Will return sparse matrix if set True else will return an array openml-python python scikit-learn sklearn sklearn_0.21.3