线性TreeShap
Linear TreeShap
摘要：
决策树由于其易于解释性而众所周知。为了提高准确性，我们需要生长深树或树的集合。这些很难解释，抵消了它们最初的好处。Shapley值最近已成为解释基于树的机器学习模型预测的流行方法。它为独立于树结构的特征提供线性加权。流行度的上升主要是由于TreeShap，它在多项式时间内解决了一般指数复杂性问题。随着在行业中的广泛采用，需要更有效的算法。本文提出了一个更有效和直接的算法：线性树形。与TreeShap一样，Linear TreeShap是精确的，需要相同的内存量。
英文摘要：
Decision trees are well-known due to their ease of interpretability. To improve accuracy, we need to grow deep trees or ensembles of trees. These are hard to interpret, offsetting their original benefits. Shapley values have recently become a popular way to explain the predictions of tree-based machine learning models. It provides a linear weighting to features independent of the tree structure. The rise in popularity is mainly due to TreeShap, which solves a general exponential complexity problem in polynomial time. Following extensive adoption in the industry, more efficient algorithms are required. This paper presents a more efficient and straightforward algorithm: Linear TreeShap. Like TreeShap, Linear TreeShap is exact and requires the same amount of memory.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
