Hands-On Machine Learning for Algorithmic Trading

Decision tree pruning

Recursive binary-splitting will likely produce good predictions on the training set but tends to overfit the data and produce poor generalization performance because it leads to overly complex trees, reflected in a large number of leaf nodes or partitioning of the feature space. Fewer splits and leaf nodes imply an overall smaller tree and often lead to better predictive performance as well as interpretability.

One approach to limit the number of leaf nodes is to avoid further splits unless they yield significant improvements of the objective metric. The downside of this strategy, however, is that sometimes splits that result in small improvements enable more valuable splits later on as the composition of the samples ...