✅ ML Algorithms Interview Questions: Part-2 🤖💬

1️⃣ Q: What is the difference between Bagging and Boosting?
🧠 A:
⦁ Bagging (e.g., Random Forest): Combines predictions from multiple models trained independently in parallel.
⦁ Boosting (e.g., XGBoost): Trains models sequentially, each learning from the previous one’s errors.
🔁 Boosting usually gives better performance but is prone to overfitting.

2️⃣ Q: Why would you choose Logistic Regression over a Tree-based model?
🧠 A:
⦁ Faster training & better interpretability
⦁ Works well with linearly separable data
⦁ Ideal for small datasets with fewer features

3️⃣ Q: How does a Decision Tree decide where to split?
🧠 A:
Uses criteria like Gini Impurity, Entropy, or Information Gain to find the feature and value that best separates the data.

4️⃣ Q: What problem does Regularization solve in Linear Regression?
🧠 A:
Prevents overfitting by penalizing large coefficients.
⦁ L1 (Lasso): Feature selection (can zero out features)
⦁ L2 (Ridge): Shrinks coefficients but keeps all features

💡 Pro Tip: Pair every algorithm with real-world use cases during interviews (e.g., Logistic Regression → churn prediction, Random Forest → credit scoring)

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