| Issue | #Downvotes for this reason | By |
|---|
| 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, and should be sorted in case of numeric values The used categories can be found in the ``categories_`` attribute | default: [[0, 1], [0, 1]] |
| dtype | Desired dtype of output handle_unknown : {'error', 'use_encoded_value'}, default='error' When set to 'error' an error will be raised in case an unknown categorical feature is present during transform. When set to 'use_encoded_value', the encoded value of unknown categories will be set to the value given for the parameter `unknown_value`. In :meth:`inverse_transform`, an unknown category will be denoted as None .. versionadded:: 0.24 | default: {"oml-python:serialized_object": "type", "value": "np.float64"} |
| encoded_missing_value | Encoded value of missing categories. If set to `np.nan`, then the `dtype` parameter must be a float dtype .. versionadded:: 1.1 | default: NaN |
| handle_unknown | default: "error" | |
| unknown_value | When the parameter handle_unknown is set to 'use_encoded_value', this parameter is required and will set the encoded value of unknown categories. It has to be distinct from the values used to encode any of the categories in `fit`. If set to np.nan, the `dtype` parameter must be a float dtype .. versionadded:: 0.24 | default: null |