From a206f38907eaa3a5f11a6df68e0d3a1e6fc66c81 Mon Sep 17 00:00:00 2001
From: ben-jaynes <1btjaynes@gmail.com>
Date: Thu, 11 Jun 2026 20:53:32 -0700
Subject: [PATCH] vault backup: 2026-06-11 20:53:32

---
 Wiki/Machine Learning/Model Validation and Evaluation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Wiki/Machine Learning/Model Validation and Evaluation.md b/Wiki/Machine Learning/Model Validation and Evaluation.md
index 315869a..45423bc 100644
--- a/Wiki/Machine Learning/Model Validation and Evaluation.md	
+++ b/Wiki/Machine Learning/Model Validation and Evaluation.md	
@@ -73,4 +73,4 @@ When using it in this context a value of 1 represents a perfect classifier (mode
 ### ROC and AUC
 ROC stands for the Receiver-operating characteristic curve. The graph is found by plotting the true positive rate on the y-axis and the false positive rate on the x-axis as the threshold changes. This is a parametric graph with the threshold as the parameter.
 ![[rocCurve.excalidraw]]
-A perfect classifier is represented by a point at $(0, 1)$ which means that every prediction is correct (no false positives)
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+A perfect classifier is represented by a point at $(0, 1)$ which means that every prediction is correct (no false positives). A straight line from $(0, 0)$ to $(1, 1)$ repres
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