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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

Chapter 4. Probabilistic Learning – Classification Using Naive Bayes

When a meteorologist provides a weather forecast, precipitation is typically described with phrases like "70 percent chance of rain." Such forecasts are known as probability of precipitation reports. Have you ever considered how they are calculated? It is a puzzling question because in reality, either it will rain or not.

Weather estimates are based on probabilistic methods, which are those concerned with describing uncertainty. They use data on past events to extrapolate future events. In the case of the weather, the chance of rain describes the proportion of prior days with similar atmospheric conditions in which precipitation occurred. A 70 percent chance of rain implies that in seven out of 10 past cases with similar conditions, precipitation occurred somewhere in the area.

This chapter covers the Naive Bayes algorithm, which uses probabilities in much the same way as a weather...

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