scikit-learn
diff --git a/‎maint_tools/test_docstrings.py
Lines changed: 0 additions & 1 deletion b/‎maint_tools/test_docstrings.py
Lines changed: 0 additions & 1 deletion
diff --git a/‎sklearn/semi_supervised/_label_propagation.py
Lines changed: 42 additions & 19 deletions b/‎sklearn/semi_supervised/_label_propagation.py
Lines changed: 42 additions & 19 deletions
@@ -14,7 +14,6 @@
 # List of modules ignored when checking for numpydoc validation.
 DOCSTRING_IGNORE_LIST = [
     "KNNImputer",
-    "LabelPropagation",
     "LabelSpreading",
     "LocallyLinearEmbedding",
     "MultiTaskElasticNetCV",
 
@@ -167,7 +167,7 @@ def _build_graph(self):
         )
 
     def predict(self, X):
-        """Performs inductive inference across the model.
+        """Perform inductive inference across the model.
 
         Parameters
         ----------
@@ -223,24 +223,28 @@ class labels.
         return probabilities
 
     def fit(self, X, y):
-        """Fit a semi-supervised label propagation model based
+        """Fit a semi-supervised label propagation model to X.
 
-        All the input data is provided matrix X (labeled and unlabeled)
-        and corresponding label matrix y with a dedicated marker value for
-        unlabeled samples.
+        The input samples (labeled and unlabeled) are provided by matrix X,
+        and target labels are provided by matrix y. We conventionally apply the
+        label -1 to unlabeled samples in matrix y in a semi-supervised
+        classification.
 
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
-            A matrix of shape (n_samples, n_samples) will be created from this.
+            Training data, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
 
         y : array-like of shape (n_samples,)
-            `n_labeled_samples` (unlabeled points are marked as -1)
-            All unlabeled samples will be transductively assigned labels.
+            Target class values with unlabeled points marked as -1.
+            All unlabeled samples will be transductively assigned labels
+            internally.
 
         Returns
         -------
         self : object
+            Returns the instance itself.
         """
         X, y = self._validate_data(X, y)
         self.X_ = X
@@ -326,7 +330,7 @@ def fit(self, X, y):
 
 
 class LabelPropagation(BaseLabelPropagation):
-    """Label Propagation classifier
+    """Label Propagation classifier.
 
     Read more in the :ref:`User Guide <label_propagation>`.
 
@@ -385,6 +389,17 @@ class LabelPropagation(BaseLabelPropagation):
     n_iter_ : int
         Number of iterations run.
 
+    See Also
+    --------
+    BaseLabelPropagation : Base class for label propagation module.
+    LabelSpreading : Alternate label propagation strategy more robust to noise.
+
+    References
+    ----------
+    Xiaojin Zhu and Zoubin Ghahramani. Learning from labeled and unlabeled data
+    with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon
+    University, 2002 http://pages.cs.wisc.edu/~jerryzhu/pub/CMU-CALD-02-107.pdf
+
     Examples
     --------
     >>> import numpy as np
@@ -398,16 +413,6 @@ class LabelPropagation(BaseLabelPropagation):
     >>> labels[random_unlabeled_points] = -1
     >>> label_prop_model.fit(iris.data, labels)
     LabelPropagation(...)
-
-    References
-    ----------
-    Xiaojin Zhu and Zoubin Ghahramani. Learning from labeled and unlabeled data
-    with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon
-    University, 2002 http://pages.cs.wisc.edu/~jerryzhu/pub/CMU-CALD-02-107.pdf
-
-    See Also
-    --------
-    LabelSpreading : Alternate label propagation strategy more robust to noise.
     """
 
     _variant = "propagation"
@@ -449,6 +454,24 @@ class distributions will exceed 1 (normalization may be desired).
         return affinity_matrix
 
     def fit(self, X, y):
+        """Fit a semi-supervised label propagation model to X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+            Target class values with unlabeled points marked as -1.
+            All unlabeled samples will be transductively assigned labels
+            internally.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
         return super().fit(X, y)