Brief

Researchers have introduced ternary decision trees, which enhance standard binary decision trees by incorporating an uncertainty zone around decision boundaries. This zone allows for weighted blending of predictions from child subtrees and flags uncertain instances for different downstream handling. Five novel methods for estimating the uncertainty zone's width were proposed and evaluated, demonstrating significant improvements in accuracy over traditional CART methods across numerous datasets. AI

Researchers have developed a method to distill large tabular foundation models (TFMs) into smaller, faster gradient-boosted tree models that can run on CPUs. This technique addresses the latency issue of TFMs, which are too slow for real-time applications like fraud scoring. By using stratified out-of-fold teacher labeling to prevent label leakage, the distilled models achieve performance close to the original TFMs but with significantly reduced inference times. AI