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Machine Learning with R

You're reading from   Machine Learning with R Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781801071321
Length 762 pages
Edition 4th Edition
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Concepts
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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 (18) Chapters Close

Preface 1. Introducing Machine Learning 2. Managing and Understanding Data FREE CHAPTER 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. Being Successful with Machine Learning 12. Advanced Data Preparation 13. Challenging Data – Too Much, Too Little, Too Complex 14. Building Better Learners 15. Making Use of Big Data 16. Other Books You May Enjoy
17. Index

Example – identifying poisonous mushrooms with rule learners

Each year, many people fall ill and sometimes even die from ingesting poisonous wild mushrooms. Since many mushrooms are very similar to each other in appearance, occasionally even experienced mushroom gatherers are poisoned.

Unlike the identification of harmful plants, such as a poison oak or poison ivy, there are no clear rules like "leaves of three, let them be" for identifying whether a wild mushroom is poisonous or edible. Complicating matters, many traditional rules such as "poisonous mushrooms are brightly colored" provide dangerous or misleading information. If simple, clear, and consistent rules were available for identifying poisonous mushrooms, they could save the lives of foragers.

As one of the strengths of rule learning algorithms is the fact that they generate easy-to-understand rules, they seem like an appropriate fit for this classification task. However, the rules will only be as...

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