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/sunUKFXMHGk

...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

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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
15:38 Summary of concepts
#statquest #ROC #AUC

Видео ROC and AUC, Clearly Explained! канала StatQuest with Josh Starmer