Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781801071321
Length 762 pages
Edition 4th Edition
Languages
Tools
Concepts
Arrow right icon
Author (1):
Arrow left icon
Brett Lantz Brett Lantz
Author Profile Icon Brett Lantz
Brett Lantz
Arrow right icon
View More author details
Toc

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

Understanding nearest neighbor classification

In a single sentence, nearest neighbor classifiers are defined by their characteristic of classifying unlabeled examples by assigning them the class of similar labeled examples. This is analogous to the dining experience described in the chapter introduction, in which a person identifies new foods through comparison to those previously encountered. With nearest neighbor classification, computers apply a human-like ability to recall past experiences to make conclusions about current circumstances. Despite the simplicity of this idea, nearest neighbor methods are extremely powerful. They have been used successfully for:

  • Computer vision applications, including optical character recognition and facial recognition in both still images and video
  • Recommendation systems that predict whether a person will enjoy a movie or song
  • Identifying patterns in genetic data to detect specific proteins or diseases

In general, nearest neighbor classifiers are...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime