Error Absoluto Medio

Error Absoluto Medio (MAE)

Media Error Absoluto (MAE) is a statistical measure used to assess the accuracy of a model’s predictions. It quantifies the average absolute difference between the actual values and the values predicted by the model. Unlike some other error metrics, MAE treats all errors equally, regardless of their direction (i.e., whether predictions are above or below the actual values).

Para calcular el MAE, sigue estos pasos:

1. Resta el valor predicho del valor real para cada observación para encontrar el error.
2. Toma el valor absoluto de cada error para evitar diferencias negativas.
3. Suma todos los errores absolutos.
4. Divide el total por el número de observaciones.

La fórmula para el MAE puede expresarse matemáticamente como:

MAE = (1/n) * Σ |Actual_i – Predicted_i|

where n is the number of observations, Real_i is the actual value, and Predicho_i es el valor predicho.

El MAE es una métrica ampliamente utilizada en campos como aprendizaje automático and forecasting because it is easy to understand and interpret. A lower MAE value indicates a better fit of the model to the data, meaning the predictions are closer to the actual values. However, it is important to note that MAE does not provide information about the direction of errors (whether predictions are overestimates or underestimates), which may be relevant in certain applications.