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Machine Learning with R

You're reading from   Machine Learning with R Expert techniques for predictive modeling to solve all your data analysis problems

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Product type Paperback
Published in Jul 2015
Publisher Packt
ISBN-13 9781784393908
Length 452 pages
Edition 2nd 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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Toc

Table of Contents (14) Chapters Close

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

An interesting new type of dining experience has been appearing in cities around the world. 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 the belief that depriving oneself of visual sensory input will enhance the sense 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...

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