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

What makes a successful machine learning model?

Until now, we have taken a largely quantitative perspective of what it means to be a successful machine learning model. Supervised learners were initially said to perform well if the accuracy was high.

In Chapter 10, Evaluating Model Performance, we expanded this definition to include other, more sophisticated performance measures, such as the Matthews correlation coefficient and the area under the ROC curve, to account for the fact that accuracy is misleading for unbalanced datasets and to consider performance trade-offs for potential use cases.

So far, we have relegated qualitative measures of model performance to the realm of unsupervised learning, although there are certainly non-quantifiable considerations in the area of predictive modeling as well. Consider, for example, a credit scoring model that is so computationally expensive that it cannot be implemented in a real-time application, or so algorithmically complex that...

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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

What makes a successful machine learning model?

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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

What makes a successful machine learning model?

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