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 – estimating the quality of wines with regression trees and model trees

Winemaking is a challenging and competitive business that offers the potential for great profit. However, there are numerous factors that contribute to the profitability of a winery. As an agricultural product, variables as diverse as the weather and the growing environment impact the quality of a varietal. The bottling and manufacturing can also affect the flavor for better or worse. Even the way the product is marketed, from the bottle design to the price point, can affect the customer's perception of taste.

As a consequence, the winemaking industry has invested heavily in data collection and machine learning methods that may assist with the decision science of winemaking. For example, machine learning has been used to discover key differences in the chemical composition of wines from different regions, and to identify the chemical factors that lead a wine to taste sweeter.

More recently, machine...

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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 – estimating the quality of wines with regression trees and model trees

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 – estimating the quality of wines with regression trees and model trees

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Authors (1)

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