What metrics do you use for regression models?

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For regression models, metrics measure how close predicted values are to actual continuous targets. Common ones include:

1. Mean Absolute Error (MAE):
   Average absolute difference between predicted and actual values. Easy to interpret in original units.

$$MAE = \frac{1}{n} \sum |y_i - \hat{y}_i|$$

2. Mean Squared Error (MSE):
   Squares errors before averaging, penalizing large errors more. Useful when large deviations are costly.

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

3. Root Mean Squared Error (RMSE):
   Square root of MSE, interpretable in same units as target. Sensitive to outliers.

4. R² (Coefficient of Determination):
   Explains variance captured by the model (0 to 1). Higher = better fit.

$$R^2 = 1 - \frac{SS_{res}}{SS_{tot}}$$

5. Adjusted R²:
   Modified R² that accounts for number of predictors, avoids overfitting.

6. Mean Absolute Percentage Error (MAPE):
   Measures average % error, good for business forecasts.

$$MAPE = \frac{100}{n} \sum \left| \frac{y_i - \hat{y}_i}{y_i} \right|$$

But fails when actual values ≈ 0.

7. Median Absolute Error:
   Robust against outliers compared to MAE.

👉 In short:

• MAE/RMSE → measure error magnitude.

• R²/Adjusted R² → measure explained variance.

• MAPE → useful for percentage-based business metrics.

Would you like me to also create a comparison table of these metrics with pros & cons for quick revision?

Read More :

What metrics do you use for classification models?

What is Naive Bayes classifier?

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