Hands-On Machine Learning for Algorithmic Trading

How to build a random forest

The random forest algorithm expands on the randomization introduced by the bootstrap samples generated by bagging to reduce variance further and improve predictive performance.

In addition to training each ensemble member on bootstrapped training data, random forests also randomly sample from the features used in the model (without replacement). Depending on the implementation, the random samples can be drawn for each tree or each split. As a result, the algorithm faces different options when learning new rules, either at the level of a tree or for each split.

The sizes of the feature samples differ for regression and classification trees:

• For classification, the sample size is typically the square root of the ...