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

View More author details

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

Understanding classification rules

Classification rules represent knowledge in the form of logical if-else statements that assign a class to unlabeled examples. They are specified in terms of an antecedent and a consequent, which form a statement stating that "if this happens, then that happens." The antecedent comprises certain combinations of feature values, while the consequent specifies the class value to assign if the rule's conditions are met. A simple rule might state, "if the hard drive is making a clicking sound, then it is about to fail."

Rule learners are a closely related sibling of decision tree learners and are often used for similar types of tasks. Like decision trees, they can be used for applications that generate knowledge for future action, such as:

Identifying conditions that lead to hardware failure in mechanical devices
Describing the key characteristics of groups of people for customer segmentation
Finding conditions that precede large...

The rest of the chapter is locked

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

Understanding classification rules

Authors (1)

Personalised recommendations for you

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

Understanding classification rules

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you