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

View More author details

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

Join our book community on Discord

https://packt.link/EarlyAccessCommunity

The late science fiction author Arthur C. Clarke wrote, "Any sufficiently advanced technology is indistinguishable from magic." This chapter covers a pair of machine learning methods that may appear at first glance to be magic. Though they are extremely powerful, their inner workings can be difficult to understand.

In engineering, these are referred to as black box processes because the mechanism that transforms the input into the output is obfuscated by an imaginary box. For instance, the black box of closed-source software intentionally conceals proprietary algorithms, the black box of political lawmaking is rooted in bureaucratic processes, and the black box of sausage making involves a bit of purposeful (but tasty) ignorance. In the case of machine learning, the black box is due to the complex mathematics allowing them to function.

Although they may not be easy to understand, it is dangerous to...

The rest of the chapter is locked

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

Join our book community on Discord

Authors (1)

Personalised recommendations for you

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

Join our book community on Discord

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you