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Applied Supervised Learning with R

You're reading from   Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends

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Product type Paperback
Published in May 2019
Publisher
ISBN-13 9781838556334
Length 502 pages
Edition 1st Edition
Languages
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Authors (2):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Karthik Ramasubramanian Karthik Ramasubramanian
Author Profile Icon Karthik Ramasubramanian
Karthik Ramasubramanian
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Table of Contents (12) Chapters Close

Applied Supervised Learning with R
Preface
1. R for Advanced Analytics FREE CHAPTER 2. Exploratory Analysis of Data 3. Introduction to Supervised Learning 4. Regression 5. Classification 6. Feature Selection and Dimensionality Reduction 7. Model Improvements 8. Model Deployment 9. Capstone Project - Based on Research Papers Appendix

Regression


Now that we have seen the machine learning workflow, we will take two widely used types of machine learning algorithms: regression and classification; both employ supervised learning to train the models. The entire theme of this book revolves around these two types of algorithms. The Beijing PM2.5 dataset will be used extensively in demonstrating both these types. The dataset will help in understanding how one can convert a regression problem into a classification problem and vice versa.

Simple and Multiple Linear Regression

Regression is one of the most useful and essential tools in analytics and econometrics (the branch of economics concerned with the use of mathematical methods, especially statistics, in describing economic systems). In many ways, modern machine learning has its roots in statistics, and one can attribute that mostly to Sir Francis Galton's work. Galton was an English Victorian-era statistician and polymath with deep interest and expertise in fields such as genetics...

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