scikit-learn
diff --git a/‎examples/neighbors/plot_regression.py
+12-9 b/‎examples/neighbors/plot_regression.py
+12-9
diff --git a/‎sklearn/neighbors/_regression.py
+4 b/‎sklearn/neighbors/_regression.py
+4
@@ -6,41 +6,44 @@
 Demonstrate the resolution of a regression problem
 using a k-Nearest Neighbor and the interpolation of the
 target using both barycenter and constant weights.
-
 """
 
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 # %%
 # Generate sample data
 # --------------------
+# Here we generate a few data points to use to train the model. We also generate
+# data in the whole range of the training data to visualize how the model would
+# react in that whole region.
 import matplotlib.pyplot as plt
 import numpy as np
 
 from sklearn import neighbors
 
-np.random.seed(0)
-X = np.sort(5 * np.random.rand(40, 1), axis=0)
-T = np.linspace(0, 5, 500)[:, np.newaxis]
-y = np.sin(X).ravel()
+rng = np.random.RandomState(0)
+X_train = np.sort(5 * rng.rand(40, 1), axis=0)
+X_test = np.linspace(0, 5, 500)[:, np.newaxis]
+y = np.sin(X_train).ravel()
 
 # Add noise to targets
 y[::5] += 1 * (0.5 - np.random.rand(8))
 
 # %%
 # Fit regression model
 # --------------------
+# Here we train a model and visualize how `uniform` and `distance`
+# weights in prediction effect predicted values.
 n_neighbors = 5
 
 for i, weights in enumerate(["uniform", "distance"]):
     knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)
-    y_ = knn.fit(X, y).predict(T)
+    y_ = knn.fit(X_train, y).predict(X_test)
 
     plt.subplot(2, 1, i + 1)
-    plt.scatter(X, y, color="darkorange", label="data")
-    plt.plot(T, y_, color="navy", label="prediction")
+    plt.scatter(X_train, y, color="darkorange", label="data")
+    plt.plot(X_test, y_, color="navy", label="prediction")
     plt.axis("tight")
     plt.legend()
     plt.title("KNeighborsRegressor (k = %i, weights = '%s')" % (n_neighbors, weights))
 
@@ -43,6 +43,10 @@ class KNeighborsRegressor(KNeighborsMixin, RegressorMixin, NeighborsBase):
 
         Uniform weights are used by default.
 
+        See the following example for a demonstration of the impact of
+        different weighting schemes on predictions:
+        :ref:`sphx_glr_auto_examples_neighbors_plot_regression.py`.
+
     algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
         Algorithm used to compute the nearest neighbors: