🟪 1-Minute Summary
Ensemble methods combine multiple models to improve performance. Two main types: (1) Bagging (parallel, reduce variance) - Random Forest, (2) Boosting (sequential, reduce bias) - AdaBoost, Gradient Boosting, XGBoost. Generally outperform single models. Trade-off: better performance but less interpretable, slower, more complex.
🟦 Core Notes (Must-Know)
What are Ensemble Methods?
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Bagging vs Boosting
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Common Ensemble Algorithms
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- Random Forest (bagging)
- AdaBoost (boosting)
- Gradient Boosting (boosting)
- XGBoost (boosting)
Why Ensembles Work
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When to Use Ensembles
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“What’s the difference between bagging and boosting?”
- [Answer: Bagging = parallel/variance reduction, Boosting = sequential/bias reduction]
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“Why do ensembles work better than single models?”
- [Answer: Wisdom of crowds, reduce errors]
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“Name 3 ensemble methods”
- [Answer: Random Forest, Gradient Boosting, XGBoost]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Using ensembles when interpretability is critical
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Mistake 2: Not tuning hyperparameters
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🟩 Mini Example (Quick Application)
Scenario
[Compare single model vs ensemble]
Solution
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
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