From d55580f9bc3b83c1e3c51546c720f2d0b4975cb2 Mon Sep 17 00:00:00 2001 From: FAQ Bot Date: Mon, 19 Oct 2026 22:58:42 +0000 Subject: [PATCH] NEW: Why use ROC AUC instead of accuracy? --- ...e3a0_why-use-roc-auc-instead-of-accuracy.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 _questions/machine-learning-zoomcamp/module-4/031_edec02e3a0_why-use-roc-auc-instead-of-accuracy.md diff --git a/_questions/machine-learning-zoomcamp/module-4/031_edec02e3a0_why-use-roc-auc-instead-of-accuracy.md b/_questions/machine-learning-zoomcamp/module-4/031_edec02e3a0_why-use-roc-auc-instead-of-accuracy.md new file mode 100644 index 00000000..e1c1a478 --- /dev/null +++ b/_questions/machine-learning-zoomcamp/module-4/031_edec02e3a0_why-use-roc-auc-instead-of-accuracy.md @@ -0,0 +1,18 @@ +--- +id: edec02e3a0 +question: Why use ROC AUC instead of accuracy? +sort_order: 31 +--- + +Accuracy depends on a chosen decision threshold (e.g., classify as positive when the predicted probability ≥ 0.5). If you change that threshold, accuracy changes too. + +ROC AUC instead measures how well your model ranks positives above negatives across *all* possible thresholds. It’s threshold-independent. + +Accuracy can also be misleading with imbalanced classes. For instance: +- If 58% of examples are positive, a dumb model that predicts “positive” for everyone gets ~58% accuracy without learning anything. +- If only 1% are positive, predicting “negative” for everyone yields ~99% accuracy while finding none of the positives. +ROC AUC isn’t fooled by this: a constant predictor scores around 0.5 (random ranking). + +A useful interpretation: AUC is the probability that a randomly chosen positive example receives a higher score than a randomly chosen negative example. + +So if your goal is to evaluate the model’s ability to separate classes by ranking (not a specific threshold), ROC AUC is usually a better choice than accuracy. \ No newline at end of file