The ROC Curve (Receiver-Operating Characteristic Curve) - Topic 84 of Machine Learning Foundations

#MLFoundations #Calculus #MachineLearning
In this video, we work through a simple example - with real numbers - to demonstrate how to calculate the Receiver-Operating Characteristic Curve (the ROC Curve), an enormously useful metric for quantifying the performance of a binary classification model.
There are eight subjects covered comprehensively in the ML Foundations series and this video is from the fourth subject, "Calculus II: Partial Derivatives & Integrals". More detail about the series and all of the associated open-source code is available at github.com/jonkrohn/ML-foundations
The playlist for the Calculus subjects is here: • Calculus for Machine L...
Jon Krohn is Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the industry’s most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at Columbia University, New York University, leading industry conferences, and online via O'Reilly.
More courses and content from Jon can be found at jonkrohn.com.