Mean Squared Error MSE
Tags: #machine learning #metricEquation
$$\text{MSE} = \frac{1}{n} \sum^{n}_{i=1} (Y_{i} - \hat{Y}_{i}) ^ {2}$$Latex Code
\text{MSE} = \frac{1}{n} \sum^{n}_{i=1} (Y_{i} - \hat{Y}_{i}) ^ {2}
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Introduction
$$ \text{MSE} $$: denotes the Mean Squared Error
$$ Y_{i} $$: denotes the true value to predict.
$$ \hat{Y}_{i} $$: denotes the predicted value as the output of a model, usually a regression model.
References
Wikipedia: Mean Squared ErrorDiscussion
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