This StatQuest video by Josh Starmer provides a clear explanation of ROC (Receiver Operating Characteristic) curves and AUC (Area Under the Curve) , which are tools used to evaluate the performance of classification models (like Logistic Regression). See: https://www.youtube.com/watch?v=4jRBRDbJemM Explanation in Words 1. The Problem: Choosing a Threshold When a machine learning model makes a prediction (e.g., "Is this mouse obese?"), it usually outputs a probability (e.g., "There is a 0.8 chance this mouse is obese"). To make a final decision, you must choose a threshold . Standard Threshold (0.5): If probability > 0.5, classify as Obese. Low Threshold (e.g., 0.1): You classify almost everyone as Obese. You catch all the actual cases (High Sensitivity), but you also falsely accuse many healthy mice (High False Positives). This is useful for dangerous diseases like Ebola where you can't afford to miss a case. High Threshold (e.g., 0.9): You are very stric...