From 98e1712918e572cea8538f81c4d2373c561440c4 Mon Sep 17 00:00:00 2001 From: Christoph Molnar Date: Mon, 28 Nov 2022 14:07:58 +0100 Subject: [PATCH] fixes #354 --- manuscript/05.9b-agnostic-shap.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manuscript/05.9b-agnostic-shap.Rmd b/manuscript/05.9b-agnostic-shap.Rmd index acfbd05df..7f09e058b 100644 --- a/manuscript/05.9b-agnostic-shap.Rmd +++ b/manuscript/05.9b-agnostic-shap.Rmd @@ -195,7 +195,7 @@ If we add an L1 penalty to the loss L, we can create sparse explanations. ### TreeSHAP -Lundberg et al. (2018)[^tree-shap] proposed TreeSHAP, a variant of SHAP for tree-based machine learning models such as decision trees, random forests and gradient boosted trees. +Lundberg et al. (2018)[^treeshap] proposed TreeSHAP, a variant of SHAP for tree-based machine learning models such as decision trees, random forests and gradient boosted trees. TreeSHAP was introduced as a fast, model-specific alternative to KernelSHAP, but it turned out that it can produce unintuitive feature attributions. TreeSHAP defines the value function using the conditional expectation $E_{X_S|X_C}(\hat{f}(x)|x_S)$ instead of the marginal expectation.