## Mean Absolute Error MAE

Tags: #machine learning #metric### Equation

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

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### Introduction

$$ \text{MAE} $$: 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 $$ e_{i} = Y_{i} - \hat{Y}_{i} $$.

### References

Wikipedia: Mean Absolute Error## Discussion

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