November 2014
Beginner to intermediate
446 pages
12h 16m
English
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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 used commonly for numeric prediction. We describe briefly the basics of regression and explain with the classic Boston Housing data set how to implement linear regression in RapidMiner. We also include a discussion on feature selection and provide some checkpoints for correctly implementing linear regression. The second is the more recent logistic regression method used for classification. We explain the basic concepts behind calculation of the logit and how this is used to transform a discrete label variable into a continuous function so that function-fitting methods may be applied. ...
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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.