Summary
In this chapter, we explored the gradient boosting algorithm, which is used to build ensembles in a sequential manner, adding a shallow decision tree that only uses a very small number of features to improve on the predictions that have been made. We saw how gradient boosting trees can be very flexibly applied to a broad range of loss functions, as well as offer many opportunities to tune the model to a given dataset and learning task.
Recent implementations have greatly facilitated the use of gradient boosting. They've done this by accelerating the training process and offering more consistent and detailed insights into the importance of features and the drivers of individual predictions.
Finally, we developed a simple trading strategy driven by an ensemble of gradient boosting models that was actually profitable, at least before significant trading costs. We also saw how to use gradient boosting with high-frequency data.
In the next chapter, we will turn...
