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Mean Absolute Error is nothing but the mean of absolute errors. If your model gave $n$ predictions $\{\hat{y}_i\}_{i = 1}^{n}$ against $n$ ground truths $\{y_i\}_{i = 1}^{n}$, then MAE is defines as follows $$MAE_{model} = \dfrac{\sum\limits_{i = 1}^{n} |y_i - \hat{y}_i|}{n} $$. Thus, MAE gives the average amount of error. So, the machine learning model with ...

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