🟪 1-Minute Summary
AdaBoost builds models sequentially, each focusing on examples the previous models got wrong by adjusting their weights. Combines weak learners (usually shallow trees/stumps) into a strong learner. Each model’s influence is weighted by its accuracy. Pros: simple, works well with weak learners. Cons: sensitive to outliers and noise, slower than Random Forest.
🟦 Core Notes (Must-Know)
How AdaBoost Works
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The Algorithm
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- Initialize sample weights
- Train weak learner
- Calculate error
- Update sample weights (increase for misclassified)
- Repeat
- Combine with weighted vote
Weak Learners
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When to Use AdaBoost
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“Explain how AdaBoost works”
- [Answer: Sequential models, reweight misclassified samples]
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“What’s a weak learner?”
- [Answer: Model slightly better than random (e.g., decision stump)]
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“AdaBoost vs Random Forest?”
- [Answer: AdaBoost = sequential/boosting, RF = parallel/bagging]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Using AdaBoost with noisy data
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Mistake 2: Not using weak learners
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🟩 Mini Example (Quick Application)
Scenario
[Binary classification with AdaBoost]
Solution
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
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