## Mean Squared Error MSE

Tags: #machine learning #metric### Equation

$$\text{MSE} = \frac{1}{n} \sum^{n}_{i=1} (Y_{i} - \hat{Y}_{i}) ^ {2}$$### 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 Error## Discussion

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