matrics_calculator.MAE
======================

.. py:module:: matrics_calculator.MAE


Functions
---------

.. autoapisummary::

   matrics_calculator.MAE.mean_absolute_error


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

.. py:function:: mean_absolute_error(y_true, y_pred)

   Calculate the Mean Absolute Error (MAE) metric for regression.

   This function computes the average absolute difference between the predicted values (`y_pred`)
   and the actual values (`y_true`). It measures the magnitude of errors in prediction, providing
   a straightforward evaluation of a model's accuracy.

   Parameters:
   ----------
   y_true : array-like
       True values of the target variable.
   y_pred : array-like
       Predicted values from the model.

   Returns:
   -------
   float
       The Mean Absolute Error.

   Notes:
   ------
   MAE is defined as:
       MAE = (1 / n) * sum(|y_true - y_pred|)
   where n is the number of observations.

   Examples:
   ---------
   >>> y_true = [100, 200, 300]
   >>> y_pred = [110, 190, 290]
   >>> mean_absolute_error(y_true, y_pred)
   10.0