Mean Squared Error

Measures numerical proximity.

Mean Squared Error

• Definition: MSE calculates the average of the squares of the errors (the differences between predicted and actual values).
• Interpretation: Like MAE, lower values are better. However, MSE is more sensitive to outliers due to the squaring of errors, which can disproportionately affect the metric. Greater error values are exaggerated.
• Formula: $\text{MSE}=\frac{1}{n}{\sum }_{i=1}^n(y_i-{\hat{y}}_i{)}^2$
• Where:
  • $n$ = number of observations
  • $y_i$ = actual value
  • ${\hat{y}}_i$ = predicted value