MAINT Fix typos found by codespell (scikit-learn#26852)

Author: DimitriPapadopoulos

doc/whats_new/older_versions.rst

@@ -344,9 +344,9 @@ API changes summary
   Please use :class:`~covariance.EllipticEnvelope` instead.
 
 - ``NeighborsClassifier`` and ``NeighborsRegressor`` are gone in the module
-  :ref:`neighbors`. Use the classes :class:`~neighors.KNeighborsClassifier`,
-  :class:`~neighors.RadiusNeighborsClassifier`, :class:`~neighors.KNeighborsRegressor`
-  and/or :class:`~neighors.RadiusNeighborsRegressor` instead.
+  :ref:`neighbors`. Use the classes :class:`~neighbors.KNeighborsClassifier`,
+  :class:`~neighbors.RadiusNeighborsClassifier`, :class:`~neighbors.KNeighborsRegressor`
+  and/or :class:`~neighbors.RadiusNeighborsRegressor` instead.
 
 - Sparse classes in the :ref:`sgd` module are now deprecated.
 

examples/bicluster/plot_spectral_biclustering.py

@@ -57,7 +57,7 @@
 
 # %%
 # We redefine the shuffled data and plot it. We observe that we lost the
-# strucuture of original data matrix.
+# structure of original data matrix.
 data = data[row_idx_shuffled][:, col_idx_shuffled]
 
 plt.matshow(data, cmap=plt.cm.Blues)

examples/cluster/plot_hdbscan.py

@@ -113,7 +113,7 @@ def plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax=
 # ----------------------
 # HDBSCAN is much more than scale invariant though -- it is capable of
 # multi-scale clustering, which accounts for clusters with varying density.
-# Traditional DBSCAN assumes that any potential clusters are homogenous in
+# Traditional DBSCAN assumes that any potential clusters are homogeneous in
 # density. HDBSCAN is free from such constraints. To demonstrate this we
 # consider the following dataset
 centers = [[-0.85, -0.85], [-0.85, 0.85], [3, 3], [3, -3]]

examples/ensemble/plot_forest_hist_grad_boosting_comparison.py

@@ -11,7 +11,7 @@
 The comparison is made by varying the parameters that control the number of
 trees according to each estimator:
 
-- `n_estimators` controls the number of trees in the forest. It's a fixed numer.
+- `n_estimators` controls the number of trees in the forest. It's a fixed number.
 - `max_iter` is the the maximum number of iterations in a gradient boosting
   based model. The number of iterations corresponds to the number of trees for
   regression and binary classification problems. Furthermore, the actual number
@@ -202,7 +202,7 @@
 # makes fitting and scoring slower. The RF model reaches such plateau earlier
 # and can never reach the test score of the largest HGBDT model.
 #
-# Note that the results shown on the above plot can change sightly across runs
+# Note that the results shown on the above plot can change slightly across runs
 # and even more significantly when running on other machines: try to run this
 # example on your own local machine.
 #

examples/linear_model/plot_ridge_coeffs.py

@@ -162,7 +162,7 @@
 # `alpha` increases, the highest coefficients shrink more rapidly, rendering
 # their corresponding features less influential in the training process. This
 # can enhance a model's ability to generalize to unseen data (if there was a lot
-# of noise to capture), but it also poses the risk of loosing performance if the
+# of noise to capture), but it also poses the risk of losing performance if the
 # regularization becomes too strong compared to the amount of noise the data
 # contained (as in this example).
 #

examples/miscellaneous/plot_display_object_visualization.py

@@ -39,7 +39,7 @@
 # Create :class:`ConfusionMatrixDisplay`
 ##############################################################################
 # With the fitted model, we compute the predictions of the model on the test
-# dataset. These predictions are used to compute the confustion matrix which
+# dataset. These predictions are used to compute the confusion matrix which
 # is plotted with the :class:`ConfusionMatrixDisplay`
 from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix
 

examples/miscellaneous/plot_metadata_routing.py

@@ -368,7 +368,7 @@ def predict(self, X, **predict_params):
 # In ``get_metadata_routing``, we add ``self`` to the routing using
 # ``add_self_request`` to indicate this estimator is consuming
 # ``sample_weight`` as well as being a router; which also adds a
-# ``$self_request`` key to the routing info as illustrated bellow. Now let's
+# ``$self_request`` key to the routing info as illustrated below. Now let's
 # look at some examples:
 
 # %%

sklearn/cluster/_hdbscan/_tree.pyx

@@ -133,7 +133,7 @@ cpdef cnp.ndarray[CONDENSED_t, ndim=1, mode='c'] _condense_tree(
         A single linkage hierarchy in scipy.cluster.hierarchy format.
 
     min_cluster_size : int, optional (default 10)
-        The minimum size of clusters to consider. Clusters smaler than this
+        The minimum size of clusters to consider. Clusters smaller than this
         are pruned from the tree.
 
     Returns

sklearn/cluster/_hdbscan/hdbscan.py

@@ -900,7 +900,7 @@ def _weighted_cluster_center(self, X):
             self.medoids_ = np.empty((n_clusters, X.shape[1]), dtype=np.float64)
 
         # Need to handle iteratively seen each cluster may have a different
-        # number of samples, hence we can't create a homogenous 3D array.
+        # number of samples, hence we can't create a homogeneous 3D array.
         for idx in range(n_clusters):
             mask = self.labels_ == idx
             data = X[mask]

sklearn/datasets/tests/test_openml.py

@@ -1534,7 +1534,7 @@ def test_fetch_openml_verify_checksum(monkeypatch, as_frame, cache, tmpdir, pars
         modified_gzip.write(data)
 
     # Requests are already mocked by monkey_patch_webbased_functions.
-    # We want to re-use that mock for all requests except file download,
+    # We want to reuse that mock for all requests except file download,
     # hence creating a thin mock over the original mock
     mocked_openml_url = sklearn.datasets._openml.urlopen