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 stepbystep. 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...
   • Machine Learning Fundamentals: The Co...  

...Sensitivity and Specificity...
   • Machine Learning Fundamentals: Sensit...  

...and the example I work through is based on Logistic Regression, so it would help to understand the basics of that as well:
   • StatQuest: Logistic Regression  

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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 (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