Q20Machine Learning
Question
Provide a comprehensive explanation of Ensemble Learning techniques. Compare Bagging and Boosting in detail.
Answer
Ensemble learning combines multiple weak learners to form a strong learner. Bagging reduces variance, while Boosting reduces bias.
### Ensemble Learning Concept Instead of relying on a single model, ensemble methods use multiple models to obtain better predictive performance. The consensus of multiple models often yields a more robust prediction.
### Bagging (Bootstrap Aggregating) Bagging builds multiple independent models in parallel using random subsets of the training data (with replacement). The final prediction is an average (regression) or majority vote (classification). Random Forest is a prime example. It primarily reduces variance and prevents overfitting.
### Boosting Boosting builds sequential models where each new model attempts to correct the errors made by the previous ones. Data points that were misclassified get higher weights. Examples include AdaBoost and Gradient Boosting. It primarily reduces bias and improves model accuracy.
