Part 6/11:
Prevents Overfitting: Encourages better generalization on unseen data.
Handles Noisy Data: Less sensitive to outliers.
Parallel Training: Models can train simultaneously, enhancing efficiency.
Ease of Implementation: Bagging is straightforward yet powerful in enhancing model accuracy.
Common applications of bagging include fraud detection, medical diagnoses, and stock market forecasting—continuous reminders of its significance in modern machine learning.