| Issue | #Downvotes for this reason | By |
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| categorical_features | 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. | default: null |
| 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 | default: null |
| drop | 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 | default: null |
| dtype | Desired dtype of output | default: {"oml-python:serialized_object": "type", "value": "np.float64"} |
| handle_unknown | 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: "ignore" |
| n_values | Number of values per feature - 'auto' : determine value range from training data | default: null |
| sparse | Will return sparse matrix if set True else will return an array | default: true |