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Machine Learning with R

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

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781801071321
Length 762 pages
Edition 4th Edition
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Author (1):
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Brett Lantz Brett Lantz
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Brett Lantz
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Table of Contents (18) Chapters Close

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

Summary

In this chapter, we learned about the basics of managing data in R. We started by taking an in-depth look at the structures used for storing various types of data. The foundational R data structure is the vector, which is extended and combined into more complex data types, such as lists and data frames. The data frame is an R data structure that corresponds to the notion of a dataset having both features and examples. R provides functions for reading and writing data frames to spreadsheet-like tabular data files.

We then explored a real-world dataset containing the prices of used cars. We examined numeric variables using common summary statistics of center and spread, and visualized relationships between prices and odometer readings with a scatterplot. Next, we examined nominal variables using tables. In examining the used car data, we followed an exploratory process that can be used to understand any dataset. These skills will be required for the other projects throughout this book.

Now that we have spent some time understanding the basics of data management with R, you are ready to begin using machine learning to solve real-world problems. In the next chapter, we will tackle our first classification task using nearest neighbor methods. You may be surprised to discover that with just a few lines of R code, a machine can achieve human-like performance on a challenging medical diagnosis task.

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Machine Learning with R - Fourth Edition
Published in: May 2023
Publisher: Packt
ISBN-13: 9781801071321
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