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 – predicting auto insurance expenses using linear regression

For an automobile insurance company to make money, it needs to collect more in membership premiums than it spends on claims paid to its beneficiaries in case of vehicle theft, damages, or loss of life in accidents. Consequently, insurers invest a great deal of time and money to develop models that accurately forecast medical expenses for the insured population. This is the field known as actuarial science, which uses sophisticated statistical techniques to estimate risk across insured populations.

Insurance expenses are difficult to predict accurately for individuals because accidents, and especially fatal accidents, are thankfully relatively rare—a bit over one fatality per 100 million vehicle miles travelled in the United States—yet, when they do happen, they are extremely costly. Moreover, the specific conditions leading to any given accident are almost completely random. An excellent driver with...

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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 – predicting auto insurance expenses using linear regression

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 – predicting auto insurance expenses using linear regression

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