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Machine Learning with R

You're reading from   Machine Learning with R R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required ‚Äì this book will take you methodically through every stage of applying machine learning.

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781782162148
Length 396 pages
Edition 1st Edition
Languages
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Author (1):
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Brett Lantz Brett Lantz
Author Profile Icon Brett Lantz
Brett Lantz
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Table of Contents (19) Chapters Close

Machine Learning with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Introducing Machine Learning FREE CHAPTER 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

Recently, I read an article describing a new type of dining experience. Patrons are served in a completely darkened restaurant by waiters who move carefully around memorized routes using only their sense of touch and sound. The allure of these establishments is rooted in the idea that depriving oneself of visual sensory input will enhance the sense of taste and smell, and foods will be experienced in new and exciting ways. Each bite is said to be a small adventure in which the diner discovers the flavors the chef has prepared.

Can you imagine how a diner experiences the unseen food? At first, there might be a rapid phase of data collection: what are the prominent spices, aromas, and textures? Does the food taste savory or sweet? Using this data, the customer might then compare the bite to the food he or she had experienced previously. Briny tastes may evoke images of seafood, while earthy tastes may be linked to past meals...

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