From c7a9ff1b150d27c819623f41487c889fd911fc7d Mon Sep 17 00:00:00 2001
From: ben-jaynes <1btjaynes@gmail.com>
Date: Thu, 11 Jun 2026 20:43:24 -0700
Subject: [PATCH] vault backup: 2026-06-11 20:43:24

---
 Wiki/Machine Learning/Model Validation and Evaluation.md | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/Wiki/Machine Learning/Model Validation and Evaluation.md b/Wiki/Machine Learning/Model Validation and Evaluation.md
index 059e578..315869a 100644
--- a/Wiki/Machine Learning/Model Validation and Evaluation.md	
+++ b/Wiki/Machine Learning/Model Validation and Evaluation.md	
@@ -70,4 +70,7 @@ $$
 \kappa = \frac{{2 \times (TP \times TN - FN \times FP)}}{(TP + FP) \times (FP + TN) + (TP + FN) \times (FN + TN)}
 $$
 When using it in this context a value of 1 represents a perfect classifier (model and reality are in perfect agreement) and 0 represents the same accuracy as random chance. The value can also go down to -1 which represents complete disagreement and worse performance than random chance.
-### AUC-ROC Curve
+### 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)
\ No newline at end of file