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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

Exploring Research Work


In this chapter, we will explore the two most significant research works that eventually also became an open source offering. The emphasis in this chapter is given onto a top-down approach, where we will start from the origin of excellent research work and see how it became a mainstream toolkit for everyone to use. While emphasizing on research work, we would like to highlight that a lot of research work does not find its place in the standard toolkit available in the market, but some gems could be found if one works slightly harder.

We recommend following the fantastic effort put by the creators of https://paperswithcode.com. The Papers With Code team has created a free and open resource platform with machine learning papers, code, and evaluation tables with the help from the community and powered by automation. They have already automated the linking of code to papers, and they are now working on automating the extraction of evaluation metrics from papers. The work...

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