DOC Replace the phrase "where n_samples in the number" with "where n_…

…samples is the number" (scikit-learn#20822)

sklearn/decomposition/_dict_learning.py

@@ -1796,7 +1796,7 @@ def fit(self, X, y=None):
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
-            Training vector, where n_samples in the number of samples
+            Training vector, where n_samples is the number of samples
             and n_features is the number of features.
 
         y : Ignored
@@ -1842,7 +1842,7 @@ def partial_fit(self, X, y=None, iter_offset=None):
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
-            Training vector, where n_samples in the number of samples
+            Training vector, where n_samples is the number of samples
             and n_features is the number of features.
 
         y : Ignored

sklearn/decomposition/_kernel_pca.py

@@ -392,7 +392,7 @@ def fit(self, X, y=None):
         Parameters
         ----------
         X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            Training vector, where n_samples in the number of samples
+            Training vector, where n_samples is the number of samples
             and n_features is the number of features.
 
         Returns
@@ -420,7 +420,7 @@ def fit_transform(self, X, y=None, **params):
         Parameters
         ----------
         X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            Training vector, where n_samples in the number of samples
+            Training vector, where n_samples is the number of samples
             and n_features is the number of features.
 
         Returns

sklearn/decomposition/_sparse_pca.py

@@ -151,7 +151,7 @@ def fit(self, X, y=None):
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
-            Training vector, where n_samples in the number of samples
+            Training vector, where n_samples is the number of samples
             and n_features is the number of features.
 
         y : Ignored
@@ -367,7 +367,7 @@ def fit(self, X, y=None):
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
-            Training vector, where n_samples in the number of samples
+            Training vector, where n_samples is the number of samples
             and n_features is the number of features.
 
         y : Ignored

sklearn/linear_model/_bayes.py

@@ -591,7 +591,7 @@ def fit(self, X, y):
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
-            Training vector, where n_samples in the number of samples and
+            Training vector, where n_samples is the number of samples and
             n_features is the number of features.
         y : array-like of shape (n_samples,)
             Target values (integers). Will be cast to X's dtype if necessary.

sklearn/linear_model/_huber.py

@@ -253,7 +253,7 @@ def fit(self, X, y, sample_weight=None):
         Parameters
         ----------
         X : array-like, shape (n_samples, n_features)
-            Training vector, where n_samples in the number of samples and
+            Training vector, where n_samples is the number of samples and
             n_features is the number of features.
 
         y : array-like, shape (n_samples,)

sklearn/manifold/_isomap.py

@@ -288,7 +288,7 @@ def fit_transform(self, X, y=None):
         Parameters
         ----------
         X : {array-like, sparse graph, BallTree, KDTree}
-            Training vector, where n_samples in the number of samples
+            Training vector, where n_samples is the number of samples
             and n_features is the number of features.
 
         y : Ignored

sklearn/svm/_base.py

@@ -1045,7 +1045,7 @@ def _fit_liblinear(
     Parameters
     ----------
     X : {array-like, sparse matrix} of shape (n_samples, n_features)
-        Training vector, where n_samples in the number of samples and
+        Training vector, where n_samples is the number of samples and
         n_features is the number of features.
 
     y : array-like of shape (n_samples,)

sklearn/svm/_bounds.py

@@ -21,7 +21,7 @@ def l1_min_c(X, y, *, loss="squared_hinge", fit_intercept=True, intercept_scalin
     Parameters
     ----------
     X : {array-like, sparse matrix} of shape (n_samples, n_features)
-        Training vector, where n_samples in the number of samples and
+        Training vector, where n_samples is the number of samples and
         n_features is the number of features.
 
     y : array-like of shape (n_samples,)

sklearn/svm/_classes.py

@@ -215,7 +215,7 @@ def fit(self, X, y, sample_weight=None):
         Parameters
         ----------
         X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            Training vector, where n_samples in the number of samples and
+            Training vector, where n_samples is the number of samples and
             n_features is the number of features.
 
         y : array-like of shape (n_samples,)
@@ -438,7 +438,7 @@ def fit(self, X, y, sample_weight=None):
         Parameters
         ----------
         X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            Training vector, where n_samples in the number of samples and
+            Training vector, where n_samples is the number of samples and
             n_features is the number of features.
 
         y : array-like of shape (n_samples,)