From 00ce212e9de977b3c70ef7ef2164f0f858a8c2ac Mon Sep 17 00:00:00 2001
From: ben-jaynes <1btjaynes@gmail.com>
Date: Tue, 11 Aug 2026 17:53:14 -0700
Subject: [PATCH] vault backup: 2026-08-11 17:53:14

---
 Wiki/Machine Learning/Imbalanced Data.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/Wiki/Machine Learning/Imbalanced Data.md b/Wiki/Machine Learning/Imbalanced Data.md
index 459e438..e0e8031 100644
--- a/Wiki/Machine Learning/Imbalanced Data.md	
+++ b/Wiki/Machine Learning/Imbalanced Data.md	
@@ -21,4 +21,6 @@ The synthetic minority oversampling technique (SMOTE) is used to generate synthe
 At the level of the algorithm there are a few approaches that can be taken to minimize the negative effects of imbalanced data.
 - Some models can weight different classes to prioritize one over another
 - The choice of model to use is important in handling imbalanced data
-	- Ensemble models such as random forests are good at handling imbalanced data
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+	- Ensemble models such as random forests are good at handling imbalanced data
+
+Algorithm-level approaches are most commonly the choice of the model used and weighting doesn't work with every model and can be challenging to get working. Knowing what model to use for different situations (especially when data is imbalanced) is very useful to get the best performance on a dataset. 
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