From ed742b24a4cfe6254e97d9d647c9fca5ed6ab3a1 Mon Sep 17 00:00:00 2001
From: ben-jaynes <1btjaynes@gmail.com>
Date: Mon, 8 Jun 2026 10:49:03 -0700
Subject: [PATCH] vault backup: 2026-06-08 10:49:03

---
 Wiki/Machine Learning/Imbalanced Data.md | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/Wiki/Machine Learning/Imbalanced Data.md b/Wiki/Machine Learning/Imbalanced Data.md
index 4ac5c7c..7dc243c 100644
--- a/Wiki/Machine Learning/Imbalanced Data.md	
+++ b/Wiki/Machine Learning/Imbalanced Data.md	
@@ -11,5 +11,10 @@ Oversampling is when synthetic data is created for the minority class to equaliz
 #### SMOTE
 The synthetic minority oversampling technique (SMOTE) is used to generate synthetic data to increase the size of a minority class. To create the synthetic data you:
 1. Choose a point in the minority class
-2. 
- ![[SMOTE.excalidraw]]
\ No newline at end of file
+2. Find k nearest minority neighbors
+3. Select j of these neighbors
+4. Create new synthetic data point along line between first point and selected neighbors
+5. Repeat
+ ![[SMOTE.excalidraw]]
+ 
+### Algorithm-Level
\ No newline at end of file