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You're reading from Machine Learning with R R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required ‚Äì this book will take you methodically through every stage of applying machine learning.

Product type Paperback

Published in Oct 2013

Publisher Packt

ISBN-13 9781782162148

Length 396 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Brett Lantz

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Table of Contents (19) Chapters

Machine Learning with R

Credits

About the Author

About the Reviewers

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Preface

1. Introducing Machine Learning FREE CHAPTER

2. Managing and Understanding Data

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. Improving Model Performance

12. Specialized Machine Learning Topics

Index

Estimating future performance

Some R machine learning packages present confusion matrices and performance measures during the model building process. The purpose of these statistics is to provide insight about the model's resubstitution error, which occurs when the training data is incorrectly predicted in spite of the model being built directly from this data. This information is intended to be used as a rough diagnostic, particularly to identify obviously poor performers.

The resubstitution error is not a very useful marker of future performance, however. For example, a model that used rote memorization to perfectly classify every training instance (that is, zero resubstitution error) would be unable to make predictions on data it has never seen before. For this reason, the error rate on the training data can be extremely optimistic about a model's future performance.

Instead of relying on resubstitution error, a better practice is to evaluate a model's performance on data it has not yet...

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You're reading from Machine Learning with R R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required ‚Äì this book will take you methodically through every stage of applying machine learning.

Table of Contents (19) Chapters

Estimating future performance

Authors (1)

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You're reading from Machine Learning with R R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required ‚Äì this book will take you methodically through every stage of applying machine learning.

Table of Contents (19) Chapters

Estimating future performance

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Authors (1)

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