clarified number of elements in reg

Author: tenosel

src/nn_regression.py

@@ -6,7 +6,7 @@
 import numpy as np
 import pandas as pd
 from sklearn.datasets import fetch_california_housing
-from sklearn.metrics import mean_absolute_error, mean_squared_error
+from sklearn.metrics import mean_squared_error
 from sklearn.model_selection import train_test_split
 from sklearn.neighbors import KNeighborsRegressor
 from sklearn.preprocessing import StandardScaler
@@ -30,7 +30,7 @@ def gs_knearest_regressor(
         (Tuple[KNeighborsRegressor, signedinteger, np.ndarray]):
         KNeighborsRegressor: The model that was trained with the optimal number of k.
         signedinteger: k that produced the smallest mean squared error on the val set.
-        np.ndarray: The array with means squared errors for k values between 1 and 39.
+        np.ndarray: The array with means squared errors for k values between 1 and 40 (40 numbers in total).
     """
     # TODO: Implement me.
     return None
@@ -101,10 +101,10 @@ def gs_knearest_regressor(
     # compute and print MSE of best estimator on test set
     # TODO
 
-    # plot mean squared error for k values between 1 and 39
+    # plot mean squared error for k values between 1 and 40
     plt.figure(figsize=(12, 6))
     plt.plot(
-        range(1, 40),
+        range(1, 41),
         error_array,
         color="red",
         linestyle="dashed",

tests/test_nn_regression.py

@@ -25,7 +25,7 @@ def test_gs_knearest_regressor() -> None:
     # Check the returned objects
     assert isinstance(knn, KNeighborsRegressor)
     assert isinstance(error_array, np.ndarray)
-    assert error_array.shape[0] == 39
+    assert error_array.shape[0] == 40
 
     # Generate a validation set
     x_val = np.random.rand(10, n_features)