From 7597f67041466bb556b05ff576d46f43100055d8 Mon Sep 17 00:00:00 2001 From: Rik Huijzer Date: Tue, 10 Oct 2023 15:11:28 +0200 Subject: [PATCH] Fix typo --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 3555540..e7d131a 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -170,7 +170,7 @@ Boston & $0.74 \pm 0.11$ & $0.70 \pm 0.05$ & $0.87 \pm 0.05$ & $0.86 \pm 0.05$ & \end{table} At the time of writing, SIRUS's predictive performance is comparable to the linear model and XGBoost on the binary classification datasets, that is, Haberman, Titanic, Breast Cancer, and Diabetes. -The best performance occurs at the Diabetes dataset where both XGBoost and the SIRUS mdoels outperform the linear model. +The best performance occurs at the Diabetes dataset where both XGBoost and the SIRUS models outperform the linear model. The reason for this could be that negative effects are often nonlinear for fragile systems [@taleb2020statistical]. For example, it could be that an increase in oral glucose tolerance increases the chance of diabetes exponentially. In such cases, the hard cutoff points chosen by tree-based models, such as XGBoost and SIRUS, may fit the data better.