Tree Ensemble (TE) models (e.g. Gradient Boosted Trees and Random Forests) often provide higher prediction performance compared to single decision trees. However, TE models generally lack transparency and interpretability, as humans have difficulty understanding their decision logic. This paper presents a novel approach to convert a TE trained for a binary classification task, to a rule list (RL) that is a global equivalent to the TE and is comprehensible for a human. This RL captures all necessary and sufficient conditions for decision making by the TE. Experiments on benchmark datasets demonstrate that, compared to state-of-the-art methods, (i) predictions from the RL generated by TE2Rules have high fidelity with respect to the original TE, (ii) the RL from TE2Rules has high interpretability measured by the number and the length of the decision rules, (iii) the run-time of TE2Rules algorithm can be reduced significantly at the cost of a slightly lower fidelity, and (iv) the RL is a fast alternative to the state-of-the-art rule-based instance-level outcome explanation techniques.
翻译:树群(TE)模型(例如,梯形推树和随机森林)往往比单条决策树提供更高的预测性能。然而,TE模型一般缺乏透明度和可解释性,因为人类难以理解其决定逻辑。本文介绍了一种新颖的方法,将受过二进制分类任务训练的TE转换成规则列表(RL),该规则列表相当于TE,对人来说可以理解。该规则列表捕捉了TE决策的所有必要和充分条件。基准数据集实验表明,与最新方法相比,TE2规则产生的RL预测对于原TE具有高度的忠诚性,(二) TE2规则产生的RL具有根据决策规则的数量和长度衡量的高可解释性,(三) TE2规则算法的运行时间可以大大降低,其成本略低一些,而且(四) RL是州基于规则的水平结果解释技术的一种快速替代方法。