November 2018
Beginner to intermediate
568 pages
15h 59m
English
Content preview from Data Science, 2nd Edition
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Chapter 5
Regression Methods
Abstract
This chapter covers two of the most popular function-fitting algorithms. The first is the well-known linear regression method, commonly used for numeric prediction. The basics of regression is briefly described and with the use of the classic Boston Housing dataset, how to implement linear regression in RapidMiner is also explained. A discussion on feature selection is also included and some checkpoints for correctly implementing linear regression are provided. The latter is the more current logistic regression method used for classification. The basic concepts behind calculation of the logit are explained along with how this is used to transform a discrete label into a continuous function so that function-fitting ...
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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.