Root Mean Squared Error (RMSE)

INFO

Square root of MSE, making it interpretable in the same unit as the target variable. Penalizes large errors.

How It Works

$$\text{RMSE}=\sqrt{\frac{1}{n}\sum _{i=1}^n{\left(y_i-{\hat{y}}_i\right)}^2}$$

• Same formula as Mean Squared Error (MSE), but square root applied
• Easier to interpret than Mean Squared Error (MSE)

What to Look For

• Lower RMSE = better performance
• More sensitive to large errors than Mean Absolute Error (MAE)
• Use when error magnitude matters

Application Models

• Logistic Regression
• Classification Decision Tree
• Random Forest Classification
• Neural Network Regression