8000 DOC Improve get_feature_names_out docstrings (#22718) · scikit-learn/scikit-learn@279388d · GitHub
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DOC Improve get_feature_names_out docstrings (#22718)
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
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‎doc/glossary.rst

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@@ -1257,7 +1257,7 @@ Methods
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be generated. If `input_features` is not passed in, then the
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`feature_names_in_` attribute will be used. If the
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`feature_names_in_` attribute is not defined, then the
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input names are named `[x0, x1, ..., x(n_features_in_)]`.
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input names are named `[x0, x1, ..., x(n_features_in_ - 1)]`.
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``get_n_splits``
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On a :term:`CV splitter` (not an estimator), returns the number of

‎sklearn/base.py

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@@ -887,7 +887,8 @@ def get_feature_names_out(self, input_features=None):
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- If `input_features` is `None`, then `feature_names_in_` is
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used as feature names in. If `feature_names_in_` is not defined,
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then names are generated: `[x0, x1, ..., x(n_features_in_)]`.
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then the following input feature names are generated:
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`["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
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- If `input_features` is an array-like, then `input_features` must
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match `feature_names_in_` if `feature_names_in_` is defined.
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‎sklearn/compose/_column_transformer.py

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@@ -463,7 +463,8 @@ def get_feature_names_out(self, input_features=None):
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- If `input_features` is `None`, then `feature_names_in_` is
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used as feature names in. If `feature_names_in_` is not defined,
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then names are generated: `[x0, x1, ..., x(n_features_in_)]`.
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then the following input feature names are generated:
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`["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
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- If `input_features` is an array-like, then `input_features` must
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match `feature_names_in_` if `feature_names_in_` is defined.
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‎sklearn/feature_selection/_base.py

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@@ -153,7 +153,8 @@ def get_feature_names_out(self, input_features=None):
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- If `input_features` is `None`, then `feature_names_in_` is
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used as feature names in. If `feature_names_in_` is not defined,
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then names are generated: `[x0, x1, ..., x(n_features_in_)]`.
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then the following input feature names are generated:
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`["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
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- If `input_features` is an array-like, then `input_features` must
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match `feature_names_in_` if `feature_names_in_` is defined.
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‎sklearn/impute/_base.py

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@@ -643,10 +643,11 @@ def get_feature_names_out(self, input_features=None):
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Input features.
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- If `input_features` is `None`, then `feature_names_in_` is
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used as feature names in. If `feature_names_in_` is not defined,
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then names are generated: `[x0, x1, ..., x(n_features_in_)]`.
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used as feature names in. If `feature_names_in_` is not defined,
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then the following input feature names are generated:
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`["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
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- If `input_features` is an array-like, then `input_features` must
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match `feature_names_in_` if `feature_names_in_` is defined.
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match `feature_names_in_` if `feature_names_in_` is defined.
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Returns
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-------
@@ -994,7 +995,8 @@ def get_feature_names_out(self, input_features=None):
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- If `input_features` is `None`, then `feature_names_in_` is
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used as feature names in. If `feature_names_in_` is not defined,
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then names are generated: `[x0, x1, ..., x(n_features_in_)]`.
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then the following input feature names are generated:
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`["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
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- If `input_features` is an array-like, then `input_features` must
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match `feature_names_in_` if `feature_names_in_` is defined.
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‎sklearn/impute/_iterative.py

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@@ -785,10 +785,11 @@ def get_feature_names_out(self, input_features=None):
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Input features.
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- If `input_features` is `None`, then `feature_names_in_` is
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used as feature names in. If `feature_names_in_` is not defined,
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then names are generated: `[x0, x1, ..., x(n_features_in_)]`.
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used as feature names in. If `feature_names_in_` is not defined,
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then the following input feature names are generated:
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`["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
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- If `input_features` is an array-like, then `input_features` must
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match `feature_names_in_` if `feature_names_in_` is defined.
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match `feature_names_in_` if `feature_names_in_` is defined.
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Returns
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-------

‎sklearn/impute/_knn.py

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@@ -350,10 +350,11 @@ def get_feature_names_out(self, input_features=None):
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Input features.
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- If `input_features` is `None`, then `feature_names_in_` is
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used as feature names in. If `feature_names_in_` is not defined,
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then names are generated: `[x0, x1, ..., x(n_features_in_)]`.
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used as feature names in. If `feature_names_in_` is not defined,
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then the following input feature names are generated:
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`["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
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- If `input_features` is an array-like, then `input_features` must
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match `feature_names_in_` if `feature_names_in_` is defined.
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match `feature_names_in_` if `feature_names_in_` is defined.
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Returns
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-------

‎sklearn/preprocessing/_discretization.py

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@@ -438,7 +438,8 @@ def get_feature_names_out(self, input_features=None):
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- If `input_features` is `None`, then `feature_names_in_` is
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used as feature names in. If `feature_names_in_` is not defined,
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then names are generated: `[x0, x1, ..., x(n_features_in_)]`.
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then the following input feature names are generated:
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`["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
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- If `input_features` is an array-like, then `input_features` must
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match `feature_names_in_` if `feature_names_in_` is defined.
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‎sklearn/preprocessing/_encoders.py

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@@ -709,7 +709,8 @@ def get_feature_names_out(self, input_features=None):
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- If `input_features` is `None`, then `feature_names_in_` is
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used as feature names in. If `feature_names_in_` is not defined,
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then names are generated: `[x0, x1, ..., x(n_features_in_)]`.
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then the following input feature names are generated:
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`["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
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- If `input_features` is an array-like, then `input_features` must
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match `feature_names_in_` if `feature_names_in_` is defined.
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‎sklearn/preprocessing/_function_transformer.py

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@@ -242,7 +242,7 @@ def get_feature_names_out(self, input_features=None):
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- If `input_features` is None, then `feature_names_in_` is
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used as the input feature names. If `feature_names_in_` is not
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defined, then names are generated:
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`[x0, x1, ..., x(n_features_in_)]`.
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`[x0, x1, ..., x(n_features_in_ - 1)]`.
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- If `input_features` is array-like, then `input_features` must
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match `feature_names_in_` if `feature_names_in_` is defined.
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‎sklearn/preprocessing/_polynomial.py

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@@ -240,7 +240,8 @@ def get_feature_names_out(self, input_features=None):
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- If `input_features is None`, then `feature_names_in_` is
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used as feature names in. If `feature_names_in_` is not defined,
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then names are generated: `[x0, x1, ..., x(n_features_in_)]`.
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then the following input feature names are generated:
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`["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
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- If `input_features` is an array-like, then `input_features` must
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match `feature_names_in_` if `feature_names_in_` is defined.
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@@ -713,7 +714,8 @@ def get_feature_names_out(self, input_features=None):
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- If `input_features` is `None`, then `feature_names_in_` is
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used as feature names in. If `feature_names_in_` is not defined,
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then names are generated: `[x0, x1, ..., x(n_features_in_)]`.
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then the following input feature names are generated:
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`["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
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- If `input_features` is an array-like, then `input_features` must
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match `feature_names_in_` if `feature_names_in_` is defined.
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‎sklearn/utils/validation.py

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@@ -1871,10 +1871,11 @@ def _check_feature_names_in(estimator, input_features=None, *, generate_names=Tr
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Input features.
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- If `input_features` is `None`, then `feature_names_in_` is
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used as feature names in. If `feature_names_in_` is not defined,
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then names are generated: `[x0, x1, ..., x(n_features_in_)]`.
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used as feature names in. If `feature_names_in_` is not defined,
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then the following input feature names are generated:
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`["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
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- If `input_features` is an array-like, then `input_features` must
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match `feature_names_in_` if `feature_names_in_` is defined.
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match `feature_names_in_` if `feature_names_in_` is defined.
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generate_names : bool, default=True
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Whether to generate names when `input_features` is `None` and

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