Merge pull request #14 from UBC-MDS/mse

edited function MSE doc

src/matrics_calculator/matrics_calculator.py

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+# Mean Squared Error (MSE) calculation
+def mean_squared_error(y_true, y_pred): 
+    """
+    Calculate the Mean Squared Error (MSE) between actual and predicted values.
+
+    This function computes the average squared difference between the predicted values (`y_pred`)
+    and the actual values (`y_true`). It is commonly used as a metric to evaluate regression models.
+    
+    Parameters
+    ----------
+    y_true : list or array-like
+        The actual observed values.
+    y_pred : list or array-like
+        The predicted values from the model.
+
+    Returns
+    -------
+    float
+        The Mean Squared Error (MSE) value, which is non-negative. 
+        A smaller value indicates that the predictions are closer to the actual values.
+
+    Notes
+    -----
+    This function assumes that the input `y_true` and `y_pred` have the same length.
+
+    Examples
+    --------
+    >>> y_true = [3, -0.5, 2, 7]
+    >>> y_pred = [2.5, 0.0, 2, 8]
+    >>> mean_squared_error(y_true, y_pred)
+    0.375
+    """
+    pass
+