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

Product type Paperback

Published in May 2023

Publisher Packt

ISBN-13 9781801071321

Length 762 pages

Edition 4th Edition

Languages

Tools

H2O

Concepts

Big Data

Author (1):

Brett Lantz

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Table of Contents (18) Chapters

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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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

Table of Contents (18) Chapters

Example – identifying poisonous mushrooms with rule learners

Authors (1)

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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

Table of Contents (18) Chapters

Example – identifying poisonous mushrooms with rule learners

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