📘 Advanced Machine Learning Tutorial – Part 1: Ensemble Learning
Ensemble learning is a powerful concept in machine learning where multiple models (often called “weak learners”) are strategically combined to improve predictive performance. The two most popular ensemble techniques are Bagging and Boosting.
🔎 What is Bagging?
Bagging (Bootstrap Aggregating) reduces variance and helps avoid overfitting. It builds multiple independent models in parallel and averages their predictions.
Random Forest is an ensemble of decision trees trained using bagging. Each tree receives a random subset of data and features.
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)⚡ What is Boosting?
Boosting reduces bias and variance by training models sequentially, where each model focuses on correcting the errors of the previous ones. Common algorithms include AdaBoost, Gradient Boosting, and XGBoost.
XGBoost is a scalable and optimized implementation of gradient boosting.
from xgboost import XGBClassifier
model = XGBClassifier()
model.fit(X_train, y_train)📊 Bagging vs Boosting Comparison
| Feature | Bagging | Boosting |
|---|---|---|
| Approach | Parallel | Sequential |
| Focus | Reduce variance | Reduce bias and variance |
| Overfitting | Lower risk | Higher risk |
📁 When to Use Bagging vs Boosting?
- Use Bagging when the model suffers from high variance (e.g., decision trees).
- Use Boosting when you need a strong predictive model, especially for structured/tabular data.
🔬 Practical Applications
Ensemble models are widely used in:
- Credit scoring
- Spam detection
- Medical diagnosis
- Customer churn prediction
🎯 Summary
- Bagging builds multiple models independently and averages the results.
- Boosting builds models sequentially, each improving the previous one.
- Popular methods include Random Forest, AdaBoost, Gradient Boosting, and XGBoost.
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