The text discusses potential bias in decision trees and random forests due to the assumption of continuous features, which can affect the modeling process. The authors demonstrate this bias through experimentation and propose a mitigation strategy by integrating out the dependency on the conditioning operator. They show that by averaging predictions using both operators, the bias can be eliminated at little cost. The bias was demonstrated to be present in sufficiently deep decision trees and random forests and the experiments show an improvement of 0.1–0.2 percentage points of r2 scores by eliminating the bias. The authors also provide a preprint for further details and a GitHub repository with the reproducible analysis.
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