Gradient Boosting Algorithm
What is Gradient Boosting?
Gradient Boosting is an ensemble machine learning technique that builds models sequentially. Each new model attempts to correct the errors made by the previous models, using gradient descent to minimize a loss function.
How It Works
- Initial Model: Start with a simple prediction (e.g., mean value)
- Calculate Residuals: Find errors from current predictions
- Fit New Model: Train a model to predict these errors
- Update: Add new model to ensemble with a learning rate
- Repeat: Continue until stopping criteria met
Key Parameters
- Learning Rate: Step size for updates (0.01-0.3)
- Number of Estimators: How many sequential models
- Max Depth: Depth of each tree
- Subsample: Fraction of data used per iteration
Advantages
- High Accuracy: Often achieves state-of-the-art results
- Handles Mixed Data: Works with numerical and categorical features
- Feature Importance: Provides variable importance rankings
- Flexible: Various loss functions for different problems
Disadvantages
- Overfitting Risk: Can overfit with too many iterations
- Slower Training: Sequential nature limits parallelization
- Parameter Sensitive: Requires careful tuning
Implementations
- XGBoost: Optimized, fast implementation
- LightGBM: Light Gradient Boosting Machine
- CatBoost: Handles categorical features natively
- Scikit-learn: GradientBoostingClassifier/Regressor
Related Terms
- XGBoost: Popular gradient boosting library
- Random Forest: Alternative ensemble method
- Hyperparameter Tuning: Optimizing gradient boosting parameters