Ensemble Methods

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🧠 Ensemble Methods in Machine Learning

🔍 What Are Ensemble Methods?

Ensemble methods combine predictions from multiple models to improve overall performance, reduce overfitting, and increase robustness.

Key Idea: “The wisdom of the crowd” – combining multiple weak learners (or strong ones) to get a better predictor.

📦 Types of Ensemble Methods

1. Bagging (Bootstrap Aggregating)

• Goal: Reduce variance
• How it works:
  • Train multiple models on random subsets of the training data (with replacement).
  • Final prediction: Majority vote (classification) or average (regression).
• Popular Algorithm: Random Forest

2. Boosting

• Goal: Reduce bias (and variance)
• How it works:
  • Models are trained sequentially, each correcting errors of the previous one.
  • More weight is given to previously misclassified instances.
• Popular Algorithms:
  • AdaBoost
  • Gradient Boosting Machines (GBM)
  • XGBoost
  • LightGBM
  • CatBoost

3. Stacking (Stacked Generalization)

• Goal: Combine different types of models
• How it works:
  • Base models are trained on the full dataset.
  • Their predictions are used as input to a meta-model, which learns how to best combine them.
• Example: Logistic regression on top of decision tree + SVM + KNN.

📊 Pros & Cons

🧪 When to Use Ensemble Methods?

• When single models underperform
• When you're dealing with high variance or high bias
• In competitions (e.g., Kaggle) – ensemble models often win

🧰 Python Libraries

• sklearn.ensemble – Bagging, Random Forest, AdaBoost, etc.
• xgboost, lightgbm, catboost – Gradient boosting
• mlxtend – For stacking

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