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

Performing feature engineering

Time, effort, and imagination are central to the process of feature engineering, which involves applying subject-matter expertise to create new features for prediction. In simple terms, it might be described as the art of making data more useful. In more complex terms, it involves a combination of domain expertise and data transformations. One needs to know not just what data will be useful to gather for the machine learning project, but also how to merge, code, and clean the data to conform to the algorithm’s expectations.

Feature engineering is closely interrelated with data exploration, as described in Chapter 11, Being Successful with Machine Learning. Both involve interrogating data through the generation and testing of hypotheses. Exploring and brainstorming are likely to lead to insights about which features will be useful for prediction, and the act of engineering the features may lead to new questions to explore.

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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

Performing feature engineering

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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

Performing feature engineering

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