how adding noisy examples changes the decision boundary of KNN

3_decision_boundary.py

@@ -24,9 +24,13 @@ print("Features: ", feature_cols, "\nTarget:", target_var)
 
 print(""" --- Train-test split ---""") # 900 examples in test set, 100 examples in train set
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=900, random_state=2)
+h = .001  # step size in the mesh
+
+# adding one noisy example
+#X_train = np.vstack((X_train, [0.25, 0.75]))
+#y_train = np.append(y_train, y_train[0])
 
 
-h = .001  # step size in the mesh
 
 # Create color maps
 cmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue'])