Hands-On Machine Learning for Algorithmic Trading

Gradient boosting machines

AdaBoost can also be interpreted as a stagewise forward approach to minimizing an exponential loss function for a binary y ∈ [-1, 1] at each iteration m to identify a new base learner h_m with the corresponding weight α_m to be added to the ensemble, as shown in the following formula:

This interpretation of the AdaBoost algorithm was only discovered several years after its publication. It views AdaBoost as a coordinate-based gradient descent algorithm that minimizes a particular loss function, namely exponential loss.

Gradient boosting leverages this insight and applies the boosting method to a much wider range of ...