Mean Absolute Error MAE
Tags: #machine learning #metricEquation
$$\text{MAE} = \frac{1}{n} \sum^{n}_{i=1} |Y_{i} - \hat{Y}_{i}| = \frac{1}{n} \sum^{n}_{i=1} |e_{i}|$$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 $$ e_{i} = Y_{i} - \hat{Y}_{i} $$.