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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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Toc

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 5. Classification

Note

Learning Objectives

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

  • Define binary classification in supervised machine learning

  • Perform binary classification using white-box models: logistic regression and decision trees

  • Evaluate the performance of supervised classification models

  • Perform binary classification using black-box ensemble models – Random Forest and XGBoost

  • Design and develop deep neural networks for classification

  • Select the best model for a given classification use case

Note

In this chapter, we will focus on solving classification use cases for supervised learning. We will use a dataset designed for a classification use case, frame a business problem around it, and explore a few popular techniques to solve the problem.

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