Deep Learning with Python by Nikhil Ketkar & Jojo Moolayil

Author:Nikhil Ketkar & Jojo Moolayil
Language: eng
Format: epub
ISBN: 9781484253649
Publisher: Apress

Mean Absolute Error

The mean absolute error (MAE) computes the mean of the absolute difference between predictions and target. The outcome, which is always positive, is a much more interpretable performance metric than MSE for regression use cases. The lower the MAE for a model the better the performance.

Mathematically, we can define MAE as

Mean Absolute Percentage Error

The mean absolute percentage error (MAPE) is the percentage equivalent of the MAE. Given its relative nature, it is by far the most interpretable performance metric for regression. The lower the MAPE for a model, the better the performance for the model.

Mathematically, we can define MAPE as

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