Uncovering Feature Interdependencies in Complex Systems with Non-Greedy Random Forests

A "non-greedy" variation of the random forest algorithm is presented to better uncover feature interdependencies inherent in complex systems. Conventionally, random forests are built from "greedy" decision trees which each consider only one split at a time during their construction. In contrast, the decision trees included in this random forest algorithm each consider three split nodes simultaneously in tiers of depth two. It is demonstrated on synthetic data and bitcoin price time series that the non-greedy version significantly outperforms the greedy one if certain non-linear relationships between feature-pairs are present. In particular, both greedy and a non-greedy random forests are trained to predict the signs of daily bitcoin returns and backtest a long-short trading strategy. The better performance of the non-greedy algorithm is explained by the presence of "XOR-like" relationships between long-term and short-term technical indicators. When no such relationships exist, performance is similar. Given its enhanced ability to understand the feature-interdependencies present in complex systems, this non-greedy extension should become a standard method in the toolkit of data scientists.