🔹 *What is Gradient Boosting?*
Gradient Boosting is a powerful *ensemble learning* technique that builds models in sequence — where each new model corrects the errors of the previous ones.
🔹 *How It Works:*
1. Start with a weak model (usually a decision tree)
2. Calculate errors (residuals)
3. Build a new tree to predict those errors
4. Combine models to minimize loss
5. Repeat for a number of iterations (or until convergence)
🔹 *Why It’s Powerful:*
✅ High predictive performance
✅ Handles missing data well
✅ Works with different types of data
✅ Reduces bias & variance effectively
🔹 *Key Concepts:*
• *Learning Rate* – Controls contribution of each model
• *Loss Function* – Guides model optimization
• *Number of Trees* – More trees = better fit (with risk of overfitting)
• *Tree Depth* – Controls complexity of each tree
• *Early Stopping* – Prevents overfitting by stopping at best iteration
🔹 *Popular Libraries:*
• XGBoost 🚀
• LightGBM ⚡
• CatBoost 🐱
• Scikit-learn (`GradientBoostingClassifier`)
🔹 *Use Cases:*
• Fraud detection
• Credit scoring
• Sales forecasting
• Customer churn prediction
• Kaggle competitions (🔥 top choice!)
🔹 *Tips:*
• Tune hyperparameters carefully
• Use `GridSearchCV` or `Optuna` for optimization
• Normalize or encode data properly
💡 *Pro Tip:* Combine Gradient Boosting with feature engineering for state-of-the-art results!