Mean Absolute Error MAE

Tags: #machine learning #metric


$$\text{MAE} = \frac{1}{n} \sum^{n}_{i=1} |Y_{i} - \hat{Y}_{i}| = \frac{1}{n} \sum^{n}_{i=1} |e_{i}|$$

Latex Code

                                 \text{MAE} = \frac{1}{n} \sum^{n}_{i=1} |Y_{i} - \hat{Y}_{i}| = \frac{1}{n} \sum^{n}_{i=1} |e_{i}|

Have Fun

Let's Vote for the Most Difficult Equation!


$$ \text{MAE} $$: denotes the Mean Absolute Error MAE
$$ Y_{i} $$: denotes the true value to predict.
$$ \hat{Y}_{i} $$: denotes the predicted value as the output of a model, usually a regression model.
$$ e_{i} $$: denotes the error as $$ e_{i} = Y_{i} - \hat{Y}_{i} $$.


Wikipedia: Mean Absolute Error


Comment to Make Wishes Come True

Leave your wishes (e.g. Passing Exams) in the comments and earn as many upvotes as possible to make your wishes come true