Description: Boosted Trees are a type of ensemble learning method that combines multiple decision trees to improve predictive performance. This approach is based on the idea that combining several models can overcome the limitations of a single decision tree, which can often be prone to overfitting. Boosted Trees work by creating a sequence of trees, where each new tree is trained to correct the errors of the previous trees. This process is known as ‘boosting’, and it allows the final model to be more robust and accurate. The main features of Boosted Trees include their ability to handle nonlinear data, their flexibility in variable selection, and their effectiveness in reducing variance and bias. Additionally, they are highly interpretable, making it easier to understand the decisions made by the model. Their relevance in the field of machine learning lies in their ability to significantly improve prediction accuracy compared to other methods, making them a valuable tool in various applications, from classification to regression.
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