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

Adapting R to handle large datasets

Although the phrase “big data” means more than just the number of rows or the amount of memory a dataset consumes, sometimes working with a large volume of data can be a challenge in itself. Large datasets can cause computers to freeze or slow to a crawl when system memory runs out, or models cannot be built in a reasonable amount of time. Many real-world datasets are very large even if they are not truly “big,” and thus you are likely to encounter some of these issues on future projects. In doing so, you may find that the task of turning data into action is more difficult than it first appeared.

Thankfully, there are packages that make it easier to work with large datasets even while remaining in the R environment. We’ll begin by looking at the functionality that allows R to connect to databases and work with datasets that may exceed available system memory, as well as packages allowing R to work in parallel...

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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

Adapting R to handle large datasets

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Table of Contents (18) Chapters

Adapting R to handle large datasets

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