**Mean Squared Error (MSE)**

Mean Squared Error (MSE) is a widely used evaluation metric for regression models, which quantifies the average squared difference between the estimated values and the actual value. The formula for MSE is given by:

$$
\text{MSE} = \frac{1}{n} \sum_{i=1}^{n} (\hat{y}_i - y_i)^2
$$


Where:
- \\( \hat{y}_i \\) represents the predicted values,
- \\( y_i \\) represents the actual values,
- \\( n \\) is the number of observations.


MSE is a measure of the quality of an estimator—it is always non-negative, and values closer to zero are better. A lower MSE indicates a better fit of the model to the data.

**Key Points:**
- **Sensitivity to Outliers**: MSE is sensitive to outliers. Large errors are penalized more due to squaring each difference, which can lead to discrepancies in model evaluation if outliers are present.
- **Units**: The units of MSE are the square of the units of the target variable, which can sometimes make interpretation difficult, particularly when comparing the MSE across different datasets.