Hands-On Machine Learning for Algorithmic Trading

DART – dropout for trees

In 2015, Rashmi and Gilad-Bachrach proposed a new model to train gradient boosting trees that aimed to address a problem they labeled over-specialization: trees added during later iterations tend only to affect the prediction of a few instances while making a minor contribution regarding the remaining instances. However, the model's out-of-sample performance can suffer, and it may become over-sensitive to the contributions of a small number of trees added earlier in the process.

The new algorithms employ dropouts which have been successfully used for learning more accurate deep neural networks where dropouts mute a random fraction of the neural connections during the learning process. As a result, nodes in higher ...