June 2018
Intermediate to advanced
224 pages
8h 3m
English
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Chapter 6. Evaluating models
This chapter covers
- Calculating model metrics
- Training versus testing data
- Recording model metrics as messages
We’re over halfway done with our exploration of the phases of a machine learning system (figure 6.1). In this chapter, we’ll consider how to evaluate models. In the context of a machine learning system, to evaluate a model means to consider its performance before making it available for use in predictions. In this chapter, we’re going to ask a lot of questions about models.
Figure 6.1. Phases of machine learning
Much of the work of evaluating models may not sound that necessary. If you’re in a hurry ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.