How does the Gradient Boosting Node create its series of decision trees?

The Gradient Boosting Node creates its series of decision trees by fitting the residuals from the predictions of the previous trees. This approach involves sequentially building trees where each subsequent tree focuses on correcting the errors made by its predecessors. Initially, a simple model is created, and from there, the algorithm calculates the residuals, which represent the difference between the actual target values and the predictions made by the model so far.

Each new tree is constructed to predict these residuals, thereby improving the overall prediction accuracy of the ensemble model. As more trees are added, they collectively work together to minimize the overall prediction error by capturing the complex relationships in the data that may not have been modeled well by the previous trees. This iterative process of fine-tuning the model is central to the effectiveness of gradient boosting, allowing it to achieve high performance on a variety of predictive tasks.