Comparison of bagging and boosting in machine learning
| Aspect | Bagging | Boosting |
| Approach | Parallel, independent base models | Sequential, adaptive base models |
| Training | Independent training of models | Sequential training of models |
| Weighting of Instances | All instances have equal weight | Misclassified instances have higher weight |
| Handling of Errors | Reduces variance and overfitting | Reduces bias and improves accuracy |
| Model Aggregation | Averages (for regression) or voting (for classification) the predictions of base models | Weighted voting based on model performance |
