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Machine Learning with R - Third Edition

You're reading from  Machine Learning with R - Third Edition

Product type Book
Published in Apr 2019
Publisher Packt
ISBN-13 9781788295864
Pages 458 pages
Edition 3rd Edition
Languages
Author (1):
Brett Lantz Brett Lantz
Profile icon Brett Lantz

Table of Contents (18) Chapters

Machine Learning with R - Third Edition
Contributors
Preface
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1. Introducing Machine Learning 2. Managing and Understanding Data 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. Improving Model Performance 12. Specialized Machine Learning Topics Index

Chapter 3. Lazy Learning – Classification Using Nearest Neighbors

A curious type of dining experience has appeared in cities around the world. Patrons are served in a completely darkened restaurant by waiters who move via memorized routes, using only their senses of touch and sound. The allure of these establishments is the belief that depriving oneself of sight will enhance the senses of taste and smell, and foods will be experienced in new ways. Each bite provides a sense of wonder while discovering the flavors the chef has prepared.

Can you imagine how a diner experiences the unseen food? Upon first bite, the senses are overwhelmed. What are the dominant flavors? Does the food taste savory or sweet? Does it taste similar to something eaten previously? Personally, I imagine this process of discovery in terms of a slightly modified adage—if it smells like a duck and tastes like a duck, then you are probably eating duck.

This illustrates an idea that can be used for machine learning—as does...

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