Online Learning of Decision Trees with Thompson Sampling

arXiv:2404.06403v1 Announce Type: new
Abstract: Decision Trees are prominent prediction models for interpretable Machine Learning. They have been thoroughly researched, mostly in the batch setting with a fixed labelled dataset, leading to popular algorithms such as C4.5, ID3 and CART. Unfortunately, these methods are of heuristic nature, they rely on greedy splits offering no guarantees of global optimality and often leading to unnecessarily complex and hard-to-interpret Decision Trees. Recent breakthroughs addressed this suboptimality issue in the batch setting, but no …

abstract algorithms arxiv cart cs.lg dataset decision decision trees machine machine learning nature online learning popular prediction prediction models sampling trees type