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Applied Supervised Learning with R

You're reading from  Applied Supervised Learning with R

Product type Book
Published in May 2019
Publisher
ISBN-13 9781838556334
Pages 502 pages
Edition 1st Edition
Languages
Authors (2):
Karthik Ramasubramanian Karthik Ramasubramanian
Profile icon Karthik Ramasubramanian
Jojo Moolayil Jojo Moolayil
Profile icon Jojo Moolayil
View More author details
Toc

Table of Contents (12) Chapters close

Applied Supervised Learning with R
Preface
1. R for Advanced Analytics 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

Summary


In this chapter, we explored different types of classification algorithms for supervised machine learning. We leveraged the Australian weather data, designed a business problem around it, and explored various machine learning techniques on the same use case. We studied how to develop these models in R and studied the functioning of these algorithms in depth with mathematical abstractions. We summarized the results from each technique and studied a generalized approach to tackle common classification use cases.

In the next chapter, we will study feature selection, dimensionality reduction, and feature engineering for machine learning models.

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