Gradient Boosting is a powerful machine learning technique used for both classification and regression problems. It builds a model in a stage-wise fashion and is known for its predictive accuracy.
Here's how it works:
- It builds trees one at a time, where each tree corrects errors of the previous one.
- Uses a loss function to improve accuracy.
- Takes into account complex non-linear relationships.
When implementing Gradient Boosting, it’s crucial to prevent overfitting by tuning parameters wisely. Popular libraries such as XGBoost, CatBoost, and LightGBM provide robust solutions for the task.