| Issue | #Downvotes for this reason | By |
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| categories | 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 .. versionadded:: 0.20 drop : {'first', 'if_binary'} or a array-like of shape (n_features,), default=None 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 However, dropping one category breaks the symmetry of the original representation and can therefore induce a bias in downstream models, for instance for penalized linear classification or regression models | default: "auto" |
| drop | default: null | |
| dtype | Desired dtype of output handle_unknown : {'error', 'ignore'}, default='error' 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. | default: {"oml-python:serialized_object": "type", "value": "np.float64"} |
| handle_unknown | default: "ignore" | |
| sparse | Will return sparse matrix if set True else will return an array | default: true |