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Machine Learning with R

You're reading from   Machine Learning with R Expert techniques for predictive modeling

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788295864
Length 458 pages
Edition 3rd Edition
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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 (16) 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 Other Books You May Enjoy
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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...

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