Helpful examples of using Gradient Boosting machine learning algorithms in scikit-learn.
The Gradient Boosting algorithm is an ensemble learning technique used for both classification and regression tasks.
It builds a predictive model in a stage-wise manner by combining multiple weak learners, typically decision trees, to form a strong learner.
The key idea is to sequentially add trees that correct the errors of the existing ensemble. Each new tree is fitted to the residual errors of the previous trees, with the goal of minimizing the overall prediction error. This is achieved through gradient descent optimization, where the algorithm seeks to minimize a specified loss function.
Gradient Boosting is highly effective at producing accurate and robust models, particularly when dealing with complex datasets. It includes variants like Gradient Boosting Machines (GBM), XGBoost, and LightGBM, which offer enhancements in speed and performance.
However, it can be computationally intensive and prone to overfitting if not properly regularized with techniques such as learning rate adjustment, tree pruning, or early stopping.