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Gradient Boosting Machine Explained in Detail
- Gradient boosting is a technique based on boosting that involves building many weak learners.
- In the case of gradient boosting, each subsequent model focuses on pseudo-residuals instead of directly on the errors of the previous one.
- Each new tree is trained to minimize the gradient of the loss function with respect to the current ensemble's predictions.
- The gradient boosting algorithm was introduced by Jerome H. Friedman in 1999 and is widely used today.
- There are numerous variations of the gradient boosting algorithm, including GBM, XGBoost, LightGBM, and CatBoost.
- In gradient boosting, trees are constructed sequentially, meaning each tree is built based on information from previously built trees.
- The algorithm for gradient boosting involves initialization, calculating pseudo-residuals, creating the next tree, and updating the model.
- An important part of the gradient boosting method is regularization by shrinkage in the update rule.
- For example, gradient boosting algorithm was explained step by step for a regression problem, with code implementation in Python.
- The quality of the regressor should be evaluated on a test set.
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