matrics_calculator.MSE
======================

.. py:module:: matrics_calculator.MSE


Functions
---------

.. autoapisummary::

   matrics_calculator.MSE.mean_squared_error


Module Contents
---------------

.. py:function:: 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.

   :param y_true: The actual observed values.
   :type y_true: list or array-like
   :param y_pred: The predicted values from the model.
   :type y_pred: list or array-like

   :returns: The Mean Squared Error (MSE) value, which is non-negative.
             A smaller value indicates that the predictions are closer to the actual values.
   :rtype: float

   .. rubric:: Notes

   This function assumes that the input `y_true` and `y_pred` have the same length.

   .. rubric:: 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