From b3535cc7b2e31b25d460b1e7b4726cd64e7a7fa1 Mon Sep 17 00:00:00 2001
From: ben-jaynes <1btjaynes@gmail.com>
Date: Thu, 18 Jun 2026 21:00:38 -0700
Subject: [PATCH] vault backup: 2026-06-18 21:00:38

---
 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 317fe46..1f928e6 100644
--- a/Wiki/Machine Learning/Imbalanced Data.md	
+++ b/Wiki/Machine Learning/Imbalanced Data.md	
@@ -17,4 +17,6 @@ The synthetic minority oversampling technique (SMOTE) is used to generate synthe
 5. Repeat
  ![[SMOTE.excalidraw]]
  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
+### Algorithm-Level
+At the level of the algorithm there are a few approaches that can be taken to minimize the negative effects of imbalanced data.
+- 
\ No newline at end of file