Description: Gradient boosting trees are an ensemble learning technique that builds models in a staged manner using decision trees as base learners. This methodology is based on the idea that predictions can be improved by successively adding new trees that correct the errors of previous trees. Each tree is trained to predict the residuals, i.e., the difference between current predictions and actual values. This approach allows the model to fit the data more accurately, as each new tree focuses on instances that were misclassified or poorly predicted by earlier trees. Gradient boosting trees are highly flexible and can handle various types of problems, including regression and classification. Additionally, they are known for their ability to prevent overfitting through techniques such as regularization and hyperparameter tuning. Their popularity has grown in the field of machine learning due to their effectiveness in data science competitions and their implementation in libraries like XGBoost and LightGBM, which optimize training performance and speed. In summary, gradient boosting trees are a powerful tool in the machine learning arsenal, offering a robust and efficient approach to modeling complex data.
