From 79636b021e21bbb4e77efdf53d2fb05b5d4470a2 Mon Sep 17 00:00:00 2001
From: ben-jaynes <1btjaynes@gmail.com>
Date: Fri, 12 Jun 2026 00:20:10 -0700
Subject: [PATCH] vault backup: 2026-06-12 00:20:10

---
 Wiki/Machine Learning/Model Validation and Evaluation.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/Wiki/Machine Learning/Model Validation and Evaluation.md b/Wiki/Machine Learning/Model Validation and Evaluation.md
index 45423bc..02abedf 100644
--- a/Wiki/Machine Learning/Model Validation and Evaluation.md	
+++ b/Wiki/Machine Learning/Model Validation and Evaluation.md	
@@ -73,4 +73,6 @@ 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). A straight line from $(0, 0)$ to $(1, 1)$ repres
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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 $(1, 1)$ represents a random classifier (same number of true and false positives). Curves will generally look like the green or blue ones with blue performing better than a random classifier and green performing worse.
+
+The area under the ROC curve (AUC) represents the probability that given random positive and negative examples it will rank the positive example above the negative one. A perfect classifier has a AUC of 1.0 meaning that it will always put a positive instance above a negative one thus separating the two classes.
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