Machine Learning - ROC and AUC, Clearly Explained!

ROC (Receiver Operator Characteristic) graphs and AUC (the area under the curve), are useful for consolidating the information from a ton of confusion matrices into a single, easy to interpret graph. This video walks you through how to create and interpret ROC graphs step-by-step. We then show how the AUC can be used to compare classification methods and, lastly, we talk about what to do when your data isn't as warm and fuzzy as it should be. NOTE: This is the 2019.07.11 revision of a video published earlier. NOTE: This video assumes you already know about Confusion Matrices... https://youtu.be/Kdsp6soqA7o ...Sensitivity and Specificity... https://youtu.be/vP06aMoz4v8 ...and the example I work through is based on Logistic Regression, so it would help to understand the basics of that as well: https://youtu.be/yIYKR4sgzI8 For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider... Patreon: https://www.patreon.com/statquest ...or... YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join ...buying one of my books, a study guide, a t-shirt or hoodie, or a song from the StatQuest store... https://statquest.org/statquest-store/ ...or just donating to StatQuest! https://www.paypal.me/statquest Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer 0:00 Awesome song and introduction 0:48 Classifying samples with logistic regression 4:03 Creating a confusion matrices for different thresholds 7:12 ROC is an alternative to tons of confusion matrices 13:44 AUC to compare different models 14:28 False Positive Rate vs Precision (Precision Recall Graphs) 15:38 Summary of concepts Correction: 12:00 The confusion matrix should be TP = 3, FP = 2, FN = 1, TN = 2. The displayed matrix should be for the next point. #statquest #ROC #AUC