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

Challenging Data – Too Much, Too Little, Too Complex

Challenging data takes many forms throughout the course of a machine learning project, and the journey of each new project represents an adventure requiring a pioneer spirit. Beginning with uncharted data that must be explored, the data must then be wrangled before it can be used with the learning algorithm. Even then, there may still be wild aspects of the data that need to be tamed for the project to be successful. Extraneous information must be culled, small-but-important details must be cultivated, and tangled webs of complexity must be cleared from the learner’s path.

Conventional wisdom in the big data era suggests that data is treasure, but as the saying goes, one can have “too much of a good thing.” Most machine learning algorithms will happily indulge in as much data as they are fed, which leads to a new set of problems akin to overeating. An abundance of data can overwhelm the learner 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

Challenging Data – Too Much, Too Little, Too Complex

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

Challenging Data – Too Much, Too Little, Too Complex

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Authors (1)

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