Addition of RMSE and Log-Cosh loss function

machine_learning/loss_functions.py

@@ -663,6 +663,38 @@ def kullback_leibler_divergence(y_true: np.ndarray, y_pred: np.ndarray) -> float
     return np.sum(kl_loss)
 
 
+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
+    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
+
+    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)
+    3.42
+    """
+    y_true, y_pred = np.array(y_true), np.array(y_pred)
+
+    rmse = np.sqrt(np.mean((y_pred - y_true) ** 2))
+
+    return rmse
+
+
 if __name__ == "__main__":
     import doctest