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DOC add n_features_in_ in the documentation (#20236)
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‎sklearn/cluster/_affinity_propagation.py‎

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@@ -312,6 +312,8 @@ class AffinityPropagation(ClusterMixin, BaseEstimator):
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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Notes
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-----
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For an example, see :ref:`examples/cluster/plot_affinity_propagation.py

‎sklearn/cluster/_agglomerative.py‎

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@@ -776,6 +776,8 @@ class AgglomerativeClustering(ClusterMixin, BaseEstimator):
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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children_ : array-like of shape (n_samples-1, 2)
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The children of each non-leaf node. Values less than `n_samples`
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correspond to leaves of the tree which are the original samples.
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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children_ : array-like of shape (n_nodes-1, 2)
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The children of each non-leaf node. Values less than `n_features`
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correspond to leaves of the tree which are the original samples.

‎sklearn/cluster/_bicluster.py‎

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@@ -261,6 +261,8 @@ class SpectralCoclustering(BaseSpectral):
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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Examples
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--------
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>>> from sklearn.cluster import SpectralCoclustering
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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Examples
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--------
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>>> from sklearn.cluster import SpectralBiclustering

‎sklearn/cluster/_birch.py‎

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@@ -404,6 +404,8 @@ class Birch(ClusterMixin, TransformerMixin, BaseEstimator):
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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See Also
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--------
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MiniBatchKMeans : Alternative implementation that does incremental updates

‎sklearn/cluster/_dbscan.py‎

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@@ -220,6 +220,8 @@ class DBSCAN(ClusterMixin, BaseEstimator):
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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Examples
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--------
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>>> from sklearn.cluster import DBSCAN

‎sklearn/cluster/_kmeans.py‎

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@@ -769,6 +769,8 @@ class KMeans(TransformerMixin, ClusterMixin, BaseEstimator):
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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See Also
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--------
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MiniBatchKMeans : Alternative online implementation that does incremental
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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See Also
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--------
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KMeans : The classic implementation of the clustering method based on the

‎sklearn/cluster/_mean_shift.py‎

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@@ -314,6 +314,8 @@ class MeanShift(ClusterMixin, BaseEstimator):
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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Examples
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>>> from sklearn.cluster import MeanShift

‎sklearn/cluster/_optics.py‎

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@@ -181,6 +181,8 @@ class OPTICS(ClusterMixin, BaseEstimator):
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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See Also
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--------
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DBSCAN : A similar clustering for a specified neighborhood radius (eps).

‎sklearn/cluster/_spectral.py‎

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@@ -421,6 +421,8 @@ class SpectralClustering(ClusterMixin, BaseEstimator):
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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Examples
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>>> from sklearn.cluster import SpectralClustering

‎sklearn/covariance/_elliptic_envelope.py‎

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@@ -83,6 +83,11 @@ class EllipticEnvelope(OutlierMixin, MinCovDet):
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Mahalanobis distances of the training set (on which :meth:`fit` is
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called) observations.
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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Examples
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>>> import numpy as np

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