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

Summary

Our findings support the popular adage that “birds of a feather flock together.” By using machine learning methods to cluster teenagers with others who have similar interests, we were able to develop a typology of teenage identities, which was predictive of personal characteristics such as gender and number of friends. These same methods can be applied to other contexts with similar results.

This chapter covered only the fundamentals of clustering. There are many variants of the k-means algorithm, as well as many other clustering algorithms, which bring unique biases and heuristics to the task. Based on the foundation in this chapter, you will be able to understand these clustering methods and apply them to new problems.

In the next chapter, we will begin to look at methods for measuring the success of a learning algorithm that are applicable across many machine learning tasks. While our process has always devoted some effort to evaluating the success...

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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

Summary

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

Summary

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