From 268ec56a86ad09b3fe779380f224924e50136577 Mon Sep 17 00:00:00 2001 From: Abhishek Chakraborty Date: Thu, 31 Aug 2023 03:15:55 -0500 Subject: [PATCH 1/4] Update glossary.yml added the term ROC curve --- glossary.yml | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/glossary.yml b/glossary.yml index fcca6b9c..40b14943 100644 --- a/glossary.yml +++ b/glossary.yml @@ -10202,3 +10202,16 @@ A hidden layer in a [neural network](#neural_network) refers to the layers of neurons that are not directly connected to input or output. The layers are "hidden" because you do not directly observe their input and output values. + +- slug: roc_curve + ref: + - machine_learning + - classification + en: + term: "ROC Curve" + def: > + An ROC curve (Receiver Operating Characteristic curve) is a graphic that displays the performance + of a binary classifier at different [classification](#classification) thresholds. The curve is + obtained by plotting the True Positive Rate (also, known as Recall or [Sensitivity](#sensitivity)) + along the vertical axis and the False Positive Rate along the horizontal axis. + From ed2784289af2197c993a88d529586d2149cbc915 Mon Sep 17 00:00:00 2001 From: Angelique Trusler Date: Thu, 12 Oct 2023 08:35:09 +0200 Subject: [PATCH 2/4] Update glossary.yml --- glossary.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/glossary.yml b/glossary.yml index 40b14943..10457eeb 100644 --- a/glossary.yml +++ b/glossary.yml @@ -10210,8 +10210,8 @@ en: term: "ROC Curve" def: > - An ROC curve (Receiver Operating Characteristic curve) is a graphic that displays the performance + A ROC curve (Receiver Operating Characteristic curve) is a graph that displays the performance of a binary classifier at different [classification](#classification) thresholds. The curve is - obtained by plotting the True Positive Rate (also, known as Recall or [Sensitivity](#sensitivity)) + obtained by plotting the True Positive Rate (also known as Recall or [Sensitivity](#sensitivity)) along the vertical axis and the False Positive Rate along the horizontal axis. From 5e1b320fd9610d65a6dd248abfcd81e1ff5686a2 Mon Sep 17 00:00:00 2001 From: Jannetta Steyn <6432530+jsteyn@users.noreply.github.com> Date: Mon, 16 Oct 2023 21:57:29 +0100 Subject: [PATCH 3/4] Update glossary.yml --- glossary.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/glossary.yml b/glossary.yml index a381c609..4e51e4df 100644 --- a/glossary.yml +++ b/glossary.yml @@ -10233,4 +10233,3 @@ of a binary classifier at different [classification](#classification) thresholds. The curve is obtained by plotting the True Positive Rate (also known as Recall or [Sensitivity](#sensitivity)) along the vertical axis and the False Positive Rate along the horizontal axis. - From fc56fbf271e64152fcbdd4ef7426dfc012780306 Mon Sep 17 00:00:00 2001 From: Jannetta Steyn <6432530+jsteyn@users.noreply.github.com> Date: Mon, 16 Oct 2023 21:57:54 +0100 Subject: [PATCH 4/4] Update glossary.yml