Decision Machines: Congruent Decision Trees

• URL: http://arxiv.org/abs/2101.11347v7
• Date: Sat, 16 Nov 2024 05:22:37 GMT
• Title: Decision Machines: Congruent Decision Trees
• Authors: Jinxiong Zhang,
• Abstract summary: We propose Decision Machines, which embed Boolean tests into a binary vector space and represent the tree structure as a matrices. We explore the congruence of decision trees and attention mechanisms, opening new avenues for optimizing decision trees and potentially enhancing their predictive power.
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• Abstract: 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.

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