论文标题
线性三重
Linear TreeShap
论文作者
论文摘要
决策树是由于其易于解释性而闻名的。为了提高准确性，我们需要种植深树或树木的合奏。这些很难解释，抵消了它们的原始好处。 Shapley值最近已成为解释基于树木的机器学习模型预测的流行方式。它为独立于树结构的特征提供了线性加权。受欢迎程度的上升主要归因于Treeshap，它解决了多项式时间中一般的指数复杂性问题。在该行业广泛采用后，需要更有效的算法。本文提出了一种更有效，更直接的算法：线性心理。像三座一样，线性三链条是精确的，需要相同数量的内存。
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.
