Decision tree and random forest classification and regression are some of the
most widely used in machine learning approaches. Binary decision tree
implementations commonly use conditioning in the form 'feature $\leq$ (or $<$)
threshold', with the threshold being the midpoint between two observed feature
values. In this paper, we investigate the bias introduced by the choice of
conditioning operator (an intrinsic property of implementations) in the
presence of features with lattice characteristics. We propose techniques to
eliminate this bias, requiring an additional prediction with decision trees and
incurring no cost for random forests. Using 20 classification and 20 regression
datasets, we demonstrate that the bias can lead to statistically significant
differences in terms of AUC and $r^2$ scores. The proposed techniques
successfully mitigate the bias, compared to the worst-case scenario,
statistically significant improvements of up to 0.1-0.2 percentage points of
AUC and $r^2$ scores were achieved and the improvement of 1.5 percentage points
of $r^2$ score was measured in the most sensitive case of random forest
regression. The implementation of the study is available on GitHub at the
following repository: https://github.com/gykovacs/conditioning_bias.

本文通过调查决策树和随机森林分类与回归中的条件操作符选择的偏差，针对具有格特征的特征的存在，提出了消除这种偏差的技术，并证明了偏差可能导致 AUC 和 r² 得分的显著差异。此外，通过提出的技术与最坏情况相比，在 AUC 和 r² 得分方面实现了统计显著的改进。