Description: Gradient boosted regression trees are a machine learning technique that combines the structure of a decision tree with the gradient boosting approach, specifically designed for regression tasks. This methodology is based on the idea of building a predictive model from multiple decision trees, where each tree is trained to correct the errors of the previous trees. Through this iterative process, the goal is to minimize the loss function, which allows for improved prediction accuracy. Gradient boosted regression trees are highly flexible and can handle both linear and nonlinear data, making them a powerful tool in the data analysis arsenal. Additionally, their ability to manage large volumes of data and their resistance to overfitting make them particularly useful in real-world applications. The technique has become popular in data science competitions and in the industry, where accuracy and interpretability are crucial. In summary, gradient boosted regression trees are an advanced extension of decision trees that optimize performance through a collaborative and adaptive approach, making them a preferred option for solving complex regression problems.
