论文标题
te2rules：使用规则解释树的合奏
TE2Rules: Explaining Tree Ensembles using Rules
论文作者
论文摘要
Tree Ensemble（TE）模型（例如梯度增强的树木）通常在表格数据集上实现最佳性能，但是它们缺乏透明度对理解决策逻辑的挑战构成了挑战。本文介绍了Te2rules（Te2rules（Tree Ensemble to Rules），这是一种通过规则列表来解释二进制分类树合奏模型的新方法，尤其是专注于解释少数类别。许多最先进的解释者都在与少数群体的解释中挣扎，使Te2rules在这种情况下很有价值。 Te2rules生成的规则紧密近似于原始模型，确保高保真度，提供了一种准确且可解释的方法来了解决策。实验结果表明，Te2rules有效地尺度到具有数百棵树的树木合奏，在跑步时间内实现了与基线相当的繁殖力。 Te2rules允许在运行时和忠诚度之间进行权衡，从而增强其实际适用性。该实现可在此处提供：https：//github.com/linkedin/te2rules。
Tree Ensemble (TE) models, such as Gradient Boosted Trees, often achieve optimal performance on tabular datasets, yet their lack of transparency poses challenges for comprehending their decision logic. This paper introduces TE2Rules (Tree Ensemble to Rules), a novel approach for explaining binary classification tree ensemble models through a list of rules, particularly focusing on explaining the minority class. Many state-of-the-art explainers struggle with minority class explanations, making TE2Rules valuable in such cases. The rules generated by TE2Rules closely approximate the original model, ensuring high fidelity, providing an accurate and interpretable means to understand decision-making. Experimental results demonstrate that TE2Rules scales effectively to tree ensembles with hundreds of trees, achieving higher fidelity within runtimes comparable to baselines. TE2Rules allows for a trade-off between runtime and fidelity, enhancing its practical applicability. The implementation is available here: https://github.com/linkedin/TE2Rules.
