November 2018
Beginner to intermediate
568 pages
15h 59m
English
Content preview from Data Science, 2nd Edition
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Chapter 8
Model Evaluation
Abstract
This chapter describes three commonly used tools for evaluating the performance of a classification algorithm. The confusion matrix will first need to be introduced and the definitions for several terms that are used in conjunction will be provided, such as sensitivity, specificity, recall, etc. How to construct receiver operating characteristic curves will then be described and when it would be appropriate to use them along with the area under the curve concept will be shown. Finally, lift and gain charts are presented, and how to construct and interpret them is shown. The RapidMiner implementation includes step-by-step processes for building each of these three very useful evaluation tools.
Keywords
Model evaluation; ...
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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.