Bagging and boosting differ mainly in how they reduce error and when youโd choose them:
- Bagging (e.g., Random Forest) trains many models independently in parallel on different bootstrap samples to reduce variance, making it ideal for unstable, high-variance models and noisy data; itโs robust, easy to tune, and rarely overfits.
- Boosting (e.g., XGBoost, LightGBM) trains models sequentially, where each new model focuses on previous mistakes to reduce bias, making it powerful for complex patterns and structured/tabular data, but more sensitive to noise and hyperparameters.
Use bagging when your model overfits, and the data is noisy; use boosting when you need maximum accuracy and can carefully tune and validate.
