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

Author: Addyk-24

machine_learning/loss_functions.py

@@ -665,28 +665,28 @@ def kullback_leibler_divergence(y_true: np.ndarray, y_pred: np.ndarray) -> float
 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.
 
-    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.  
-    The RMSE value is always non-negative, and a lower RMSE indicates better model performance.
-
-    RMSE =  sqrt( (1/n) * Σ (y_true - y_pred) ^ 2)
+    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
+        float: The RMSE Loss function between y_pred and y_true
     
     Example:
         >>> y_true = np.array([100, 200, 300])
         >>> y_pred = np.array([110, 190, 310])
-        >>> rmse(A_t, F_t)
+        >>> rmse(y_true, y_pred)
         3.42
-
     """
     y_true, y_pred = np.array(y_true), np.array(y_pred)