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