What are ensemble methods? Explain bagging and boosting.

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🔹 What are Ensemble Methods?

Ensemble methods are machine learning techniques that combine multiple models (often called "weak learners") to produce a more accurate and robust predictive model.

• The idea is that a group of models working together performs better than any single model alone.

• Ensembles reduce variance, bias, or improve prediction stability.

🔹 Two Popular Ensemble Methods: Bagging and Boosting

1. Bagging (Bootstrap Aggregating)

• Bagging builds multiple models independently and combines their predictions.

• Steps:

  1. Create multiple subsets of the training data by sampling with replacement (bootstrap sampling).

  2. Train a separate model (often decision trees) on each subset.

  3. Aggregate results (majority voting for classification, averaging for regression).

• Example: Random Forest is a popular bagging algorithm.

• Goal: Reduce variance and prevent overfitting.

2. Boosting

• Boosting builds models sequentially, where each new model tries to fix the errors of the previous ones.

• Steps:

  1. Train an initial weak model.

  2. Identify errors or misclassified points.

  3. Give more weight to these difficult cases.

  4. Train the next model to focus on correcting them.

  5. Combine all models’ outputs into a strong learner.

• Example: AdaBoost, Gradient Boosting, XGBoost, LightGBM are well-known boosting algorithms.

• Goal: Reduce bias and improve accuracy by focusing on hard-to-predict examples.

🔹 Key Differences

• Bagging: Builds models in parallel, reduces variance, good for overfitting.

• Boosting: Builds models sequentially, reduces bias, focuses on difficult data points.

✅ In short:
Ensemble methods combine multiple models for better performance. Bagging creates independent models and aggregates them to reduce variance, while Boosting builds models sequentially, correcting mistakes step by step to reduce bias and improve accuracy.

Read More :

How does a decision tree algorithm work?

What are precision, recall, and F1-score?

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