matrics_calculator.MAPE

Functions

mean_absolute_percentage_error(y_true, y_pred)

Calculate the Mean Absolute Percentage Error (MAPE) metric for regression.

Module Contents

matrics_calculator.MAPE.mean_absolute_percentage_error(y_true, y_pred)[source]

Calculate the Mean Absolute Percentage Error (MAPE) metric for regression.

This function computes the average percentage difference between the predicted values (y_pred) and the actual values (y_true). It measures the relative magnitude of errors in prediction, expressed as a percentage. MAPE is widely used to evaluate regression models, especially when relative error matters more than absolute error.

Parameters:

y_truearray-like: True values of the target variable.
y_predarray-like: Predicted values from the model.

Returns:

float: The Mean Absolute Percentage Error (as a percentage).

Notes:

MAPE is defined as:: MAPE = (1 / n) * sum(|(y_true - y_pred) / y_true|) * 100

where n is the number of observations.

Examples:

>>> y_true = [100, 200, 300]
>>> y_pred = [110, 190, 290]
>>> mean_absolute_percentage_error(y_true, y_pred)
3.3333