Decision Machines: Congruent Decision Trees
This work addresses the limitations of decision trees for machine learning practitioners by introducing a novel representation that could enhance optimization and predictive power, though it appears incremental as it builds on existing tree and attention concepts.
The paper tackles the problem of decision trees lacking parameterized representation, which leads to overfitting and difficulty in finding optimal structures, by proposing Decision Machines that embed Boolean tests into a binary vector space and represent tree structures as matrices, enabling interleaved traversal through matrix computation and exploring congruence with attention mechanisms.
The decision tree recursively partitions the input space into regions and derives axis-aligned decision boundaries from data. Despite its simplicity and interpretability, decision trees lack parameterized representation, which makes it prone to overfitting and difficult to find the optimal structure. We propose Decision Machines, which embed Boolean tests into a binary vector space and represent the tree structure as a matrices, enabling an interleaved traversal of decision trees through matrix computation. Furthermore, we explore the congruence of decision trees and attention mechanisms, opening new avenues for optimizing decision trees and potentially enhancing their predictive power.