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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

Chapter 3. Introduction to Supervised Learning

Note

Learning Objectives

By the end of this chapter, you will be able to:

  • Explain supervised learning and machine learning workflow

  • Use and explore the Beijing PM2.5 dataset

  • Explain the difference between continuous and categorical dependent variables

  • Implement the basic regression and classification algorithms in R

  • Identify the key differences between supervised learning and other types of machine learning

  • Work with the evaluation metrics of supervised learning algorithms

  • Perform model diagnostics for avoiding biased coefficient estimates and large standard errors

Note

In this chapter, we will introduce supervised learning and demonstrate the workflow of building machine learning models with real-world examples.

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