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Mastering Machine Learning with R

You're reading from   Mastering Machine Learning with R Advanced machine learning techniques for building smart applications with R 3.5

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Product type Paperback
Published in Jan 2019
Publisher
ISBN-13 9781789618006
Length 354 pages
Edition 3rd Edition
Languages
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Table of Contents (16) Chapters Close

Preface 1. Preparing and Understanding Data 2. Linear Regression FREE CHAPTER 3. Logistic Regression 4. Advanced Feature Selection in Linear Models 5. K-Nearest Neighbors and Support Vector Machines 6. Tree-Based Classification 7. Neural Networks and Deep Learning 8. Creating Ensembles and Multiclass Methods 9. Cluster Analysis 10. Principal Component Analysis 11. Association Analysis 12. Time Series and Causality 13. Text Mining 14. Creating a Package 15. Other Books You May Enjoy

K-nearest neighbors

In our previous efforts, we built models that had coefficients or, to put it in another way, parameter estimates for each of our included features. With KNN, we have no parameters as the learning method is so-called instance-based learning. In short, labeled examples (inputs and corresponding output labels) are stored, and no action is taken until a new input pattern demands an output value (Battiti and Brunato, 2014, p. 11). This method is commonly called lazy learning, as no specific model parameters are produced. The train instances themselves represent the knowledge. For the prediction of any new instance (a new data point), the training data is searched for an instance that most resembles the new instance in question. KNN does this for a classification problem by looking at the closest points—the nearest neighbors—to determine the proper...

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