Hands-On Machine Learning for Algorithmic Trading

How to train and tune a random forest

The key configuration parameters include the various hyperparameters for the individual decision trees introduced in the section How to tune the hyperparameters. The following tables lists additional options for the two RandomForest classes:

Keyword	Default	Description
bootstrap	True	Bootstrap samples during training.
n_estimators	10	Number of trees in the forest.
oob_score	False	Uses out-of-bag samples to estimate the R2 on unseen data.

The bootstrap parameter activates in the preceding bagging algorithm outline, which in turn enables the computation of the out-of-bag score (oob_score) that estimates the generalization accuracy using samples not included in the bootstrap sample used to train ...