You're reading from Machine Learning with R Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data

Product type Paperback

Published in May 2023

Publisher Packt

ISBN-13 9781801071321

Length 762 pages

Edition 4th Edition

Languages

Tools

H2O

Concepts

Big Data

Author (1):

Brett Lantz

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Table of Contents (18) Chapters

Preface

1. Introducing Machine Learning

2. Managing and Understanding Data FREE CHAPTER

3. Lazy Learning – Classification Using Nearest Neighbors

4. Probabilistic Learning – Classification Using Naive Bayes

5. Divide and Conquer – Classification Using Decision Trees and Rules

6. Forecasting Numeric Data – Regression Methods

7. Black-Box Methods – Neural Networks and Support Vector Machines

8. Finding Patterns – Market Basket Analysis Using Association Rules

9. Finding Groups of Data – Clustering with k-means

10. Evaluating Model Performance

11. Being Successful with Machine Learning

12. Advanced Data Preparation

13. Challenging Data – Too Much, Too Little, Too Complex

14. Building Better Learners

15. Making Use of Big Data

16. Other Books You May Enjoy

17. Index

Exploring R’s tidyverse

A new approach has rapidly taken shape as the dominant paradigm for working with data in R. Championed by Hadley Wickham—the mind behind many of the packages that drove much of R’s initial surge in popularity—this new wave is now backed by a much larger team at Posit (formerly known as RStudio). The company’s user-friendly RStudio Desktop application integrates nicely into this new ecosystem, known as the tidyverse, because it provides a universe of packages devoted to tidy data. The entire suite of tidyverse packages can be installed with the install.packages("tidyverse") command.

A growing number of resources are available online to learn more about the tidyverse, starting with its homepage at https://www.tidyverse.org. Here, you can learn about the various packages included in the set, a few of which will be described in this chapter. Additionally, the book R for Data Science by Hadley Wickham and Garrett...

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You're reading from Machine Learning with R Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data

Table of Contents (18) Chapters

Exploring R’s tidyverse

Authors (1)

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You're reading from Machine Learning with R Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data

Table of Contents (18) Chapters

Exploring R’s tidyverse

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Authors (1)

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