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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 and Classification Problems


We see classification and regression problems all around us in our daily life. The chances of rain from https://weather.com, our emails getting filtered into the spam mailbox and inbox, our personal and home loans getting accepted or rejected, deciding to pick our next holiday destination, exploring the options for buying a new house, investment decisions to gain short- and long-term benefits, purchasing the next book from Amazon; the list goes on and on. The world around us today is increasingly being run by algorithms that help us with our choices (which is not always a good thing).

As discussed in Chapter 2, Exploratory Analysis of Data, we will use the Minto Pyramid principle called Situation–Complication–Question (SCQ) to define our problem statement. The following table shows the SCQ approach for Beijing's PM2.5 problem:

Figure 3.3: Applying SCQ on Beijing's PM2.5 problem.

Now, in the SCQ construct described in the previous table, we can do a simple...

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