Adding RMSE - Root Mean Squared Error Loss function for ML Evaluation

Author: Addyk-24

machine_learning/loss_functions.py

@@ -666,32 +666,44 @@ def root_mean_squared_error(y_true, y_pred):
     """
     Root Mean Squared Error (RMSE)
 
-    Root Mean Squared Error (RMSE) is a standard metric used to evaluate
-    the accuracy of regression models.
-    It measures the average magnitude of the prediction errors, giving
+    Root Mean Squared Error (RMSE) is a standard metric,
+    it measures the average magnitude of the prediction errors, giving
     higher weight to larger errors due to squaring.
 
     RMSE = sqrt( (1/n) * Σ (y_true - y_pred) ^ 2)
 
     Reference: https://en.wikipedia.org/wiki/Root_mean_square_deviation
 
     Parameters:
-        y_pred: Predicted Value
-        y_true: Actual Value
+    - y_pred: Predicted Value
+    - y_true: Actual Value
 
     Returns:
         float: The RMSE Loss function between y_pred and y_true
 
-    >>> y_true = np.array([100, 200, 300])
-    >>> y_pred = np.array([110, 190, 310])
-    >>> rmse(y_true, y_pred)
+    >>> true_labels = np.array([100, 200, 300])
+    >>> predicted_probs = np.array([110, 190, 310])
+    >>> root_mean_squared_error(true_labels, predicted_probs)
     3.42
+
+    >>> true_labels = [2, 4, 6, 8]
+    >>> predicted_probs = [3, 5, 7, 10]
+    >>> root_mean_squared_error(true_labels, predicted_probs)
+    1.2247
+
+    >>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
+    >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
+    >>> root_mean_squared_error(true_labels, predicted_probs)
+    Traceback (most recent call last):
+    ...
+    ValueError: Input arrays must have the same length.
     """
+    if len(y_true) != len(y_pred):
+        raise ValueError("Input arrays must have the same length.")
     y_true, y_pred = np.array(y_true), np.array(y_pred)
 
-    rmse = np.sqrt(np.mean((y_pred - y_true) ** 2))
-
-    return rmse
+    mse = np.mean((y_pred - y_true) ** 2)
+    return np.sqrt(mse)
 
 
 if __name__ == "__main__":