From 3b8d647184094efbd664eed500a68c2b286ce18f Mon Sep 17 00:00:00 2001
From: ben-jaynes <1btjaynes@gmail.com>
Date: Mon, 8 Jun 2026 13:16:43 -0700
Subject: [PATCH] vault backup: 2026-06-08 13:16:43

---
 Wiki/Machine Learning/Imbalanced Data.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Wiki/Machine Learning/Imbalanced Data.md b/Wiki/Machine Learning/Imbalanced Data.md
index d56b17f..317fe46 100644
--- a/Wiki/Machine Learning/Imbalanced Data.md	
+++ b/Wiki/Machine Learning/Imbalanced Data.md	
@@ -16,5 +16,5 @@ The synthetic minority oversampling technique (SMOTE) is used to generate synthe
 4. Create new synthetic data point along line between first point and selected neighbors
 5. Repeat
  ![[SMOTE.excalidraw]]
- SMOTE can be helpful to increase the size of a minority dataset but 
+ SMOTE can be helpful to increase the size of a minority dataset but has a few limitations. If the minority class is too small SMOTE can overgeneralize. It also does not work very well with categorical data and can create some data points with values that don't make sense for the feature. There is also no way for it to predict/generate outliers which could cause a model to overfit the data.
 ### Algorithm-Level
\ No newline at end of file