\n",
" \n",
" \n",
" | \n",
" \n",
" \n",
" categories\n",
" categories: 'auto' or a list of array-like, default='auto'
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\n",
" \n",
" | \n",
" 'auto' | \n",
"
\n",
" \n",
"\n",
" \n",
" | \n",
" \n",
" \n",
" drop\n",
" drop: {'first', 'if_binary'} or an 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 an unregularized linear regression model.
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.
- None : retain all features (the default).
- 'first' : drop the first category in each feature. If only one
category is present, the feature will be dropped entirely.
- 'if_binary' : drop the first category in each feature with two
categories. Features with 1 or more than 2 categories are
left intact.
- array : ``drop[i]`` is the category in feature ``X[:, i]`` that
should be dropped.
When `max_categories` or `min_frequency` is configured to group
infrequent categories, the dropping behavior is handled after the
grouping.
.. versionadded:: 0.21
The parameter `drop` was added in 0.21.
.. versionchanged:: 0.23
The option `drop='if_binary'` was added in 0.23.
.. versionchanged:: 1.1
Support for dropping infrequent categories.\n",
" \n",
" | \n",
" None | \n",
"
\n",
" \n",
"\n",
" \n",
" | \n",
" \n",
" \n",
" sparse_output\n",
" sparse_output: bool, default=True
When ``True``, it returns a :class:`scipy.sparse.csr_matrix`,
i.e. a sparse matrix in \"Compressed Sparse Row\" (CSR) format.
.. versionadded:: 1.2
`sparse` was renamed to `sparse_output`\n",
" \n",
" | \n",
" True | \n",
"
\n",
" \n",
"\n",
" \n",
" | \n",
" \n",
" \n",
" dtype\n",
" dtype: number type, default=np.float64
Desired dtype of output.\n",
" \n",
" | \n",
" <class 'numpy.float64'> | \n",
"
\n",
" \n",
"\n",
" \n",
" | \n",
" \n",
" \n",
" handle_unknown\n",
" handle_unknown: {'error', 'ignore', 'infrequent_if_exist', 'warn'}, default='error'
Specifies the way unknown categories are handled during :meth:`transform`.
- 'error' : Raise an error if an unknown category is present during transform.
- 'ignore' : When 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.
- 'infrequent_if_exist' : When an unknown category is encountered
during transform, the resulting one-hot encoded columns for this
feature will map to the infrequent category if it exists. The
infrequent category will be mapped to the last position in the
encoding. During inverse transform, an unknown category will be
mapped to the category denoted `'infrequent'` if it exists. If the
`'infrequent'` category does not exist, then :meth:`transform` and
:meth:`inverse_transform` will handle an unknown category as with
`handle_unknown='ignore'`. Infrequent categories exist based on
`min_frequency` and `max_categories`. Read more in the
:ref:`User Guide `.
- 'warn' : When an unknown category is encountered during transform
a warning is issued, and the encoding then proceeds as described for
`handle_unknown=\"infrequent_if_exist\"`.
.. versionchanged:: 1.1
`'infrequent_if_exist'` was added to automatically handle unknown
categories and infrequent categories.
.. versionadded:: 1.6
The option `\"warn\"` was added in 1.6.\n",
" \n",
" | \n",
" 'error' | \n",
"
\n",
" \n",
"\n",
" \n",
" | \n",
" \n",
" \n",
" min_frequency\n",
" min_frequency: int or float, default=None
Specifies the minimum frequency below which a category will be
considered infrequent.
- If `int`, categories with a smaller cardinality will be considered
infrequent.
- If `float`, categories with a smaller cardinality than
`min_frequency * n_samples` will be considered infrequent.
.. versionadded:: 1.1
Read more in the :ref:`User Guide `.\n",
" \n",
" | \n",
" None | \n",
"
\n",
" \n",
"\n",
" \n",
" | \n",
" \n",
" \n",
" max_categories\n",
" max_categories: int, default=None
Specifies an upper limit to the number of output features for each input
feature when considering infrequent categories. If there are infrequent
categories, `max_categories` includes the category representing the
infrequent categories along with the frequent categories. If `None`,
there is no limit to the number of output features.
.. versionadded:: 1.1
Read more in the :ref:`User Guide `.\n",
" \n",
" | \n",
" None | \n",
"
\n",
" \n",
"\n",
" \n",
" | \n",
" \n",
" \n",
" feature_name_combiner\n",
" feature_name_combiner: \"concat\" or callable, default=\"concat\"
Callable with signature `def callable(input_feature, category)` that returns a
string. This is used to create feature names to be returned by
:meth:`get_feature_names_out`.
`\"concat\"` concatenates encoded feature name and category with
`feature + \"_\" + str(category)`.E.g. feature X with values 1, 6, 7 create
feature names `X_1, X_6, X_7`.
.. versionadded:: 1.3\n",
" \n",
" | \n",
" 'concat' | \n",
"
\n",
" \n",
" \n",
"