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Mastering Machine Learning with R

You're reading from   Mastering Machine Learning with R Master machine learning techniques with R to deliver insights for complex projects

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781783984527
Length 400 pages
Edition 1st Edition
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Table of Contents (15) Chapters Close

Preface 1. A Process for Success 2. Linear Regression – The Blocking and Tackling of Machine Learning FREE CHAPTER 3. Logistic Regression and Discriminant Analysis 4. Advanced Feature Selection in Linear Models 5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines 6. Classification and Regression Trees 7. Neural Networks 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis and Recommendation Engines 11. Time Series and Causality 12. Text Mining A. R Fundamentals Index

Introduction

This quote from Fernández-Delgado et al. in the Journal of Machine Learning Research is meant to set the stage that the techniques in this chapter are quite powerful, particularly when used for classification problems. Certainly, they are not always the best solution but they do provide a good starting point.

In the previous chapters, we examined the techniques to predict either a quantity or a label classification. Here we will apply them on both types of problems. We will also approach the business problem differently than in the previous chapters. Instead of defining a new problem, we will apply the techniques to some of the issues that we already tackled, with an eye to see if we can improve our predictive power. For all intents and purposes, the business case in this chapter is to see if we can improve on the models that we selected before.

The first item of discussion is the basic decision tree, which is both simple to build and to understand. However, the single...

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