From the course: Deep Learning with Python: Hands-On Introduction to Deep Learning Models

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Evaluating a deep learning model: Recall

Evaluating a deep learning model: Recall - Python Tutorial

From the course: Deep Learning with Python: Hands-On Introduction to Deep Learning Models

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Evaluating a deep learning model: Recall

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- [Instructor] In this video, you will learn how to generate and interpret the recall of a deep learning model. I'll be writing the code in the 05_04E file. You can follow along by completing the empty code cells in the 05_04B file. Note that this video is the fourth in a five video sequence that walks students through the process of evaluating a deep learning model in Python. If you have not done so, watch the previous three videos for a detailed explanation of the prior code. Before we begin, let's run the code we created in those videos to get our environment up to speed. So I'm going to scroll up to make sure that our code is complete before we proceed. Okay, so that's done. Now we can get back down to where we were. So recall, also known as sensitivity or true positive rate, is a metric that measures how well a model can identify actual positive instances. It answers the question, of all the actual positive instances how many did the model correctly identify? The formula for…

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