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Machine Learning with R

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

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781801071321
Length 762 pages
Edition 4th Edition
Languages
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Concepts
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Author (1):
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Brett Lantz Brett Lantz
Author Profile Icon Brett Lantz
Brett Lantz
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Table of Contents (19) Chapters Close

Preface 1. Thinking Computationally 2. Abstraction in Detail FREE CHAPTER 3. Algorithmic Thinking and Complexity 4. Understanding the Machine 5. Data Structures 6. Reusing Your Code and Modularity 7. Outlining the Challenge 8. Building a Simple Command-Line Interface 9. Reading Data from Different Formats 10. Finding Information in Text 11. Clustering Data 12. Reflecting on What We Have Built 13. The Problems of Scale 14. Dealing with GPUs and Specialized Hardware 15. Profiling Your Code 16. Unlock Your Exclusive Benefits 17. Other Books You May Enjoy 18. Index

Summary

In this chapter, we discussed various issues that one might encounter when building a command-line interface for an application. The most important part is parsing options and positional arguments provided by the user, implemented here using the Boost program options library. This library parses the argv array passed to the application, the environment variables, and configuration files (although we don’t use this functionality here). We also covered setting up logging (using the spdlog library), error handling, signal handling, and formatting the results to be printed to the terminal, or otherwise.

There are many ways to design a command-line interface, and there is a lot that can be done using such an interface. It is important that you have a sensible, predictable, and functional interface even if you are the only one who will use it. The addition of help and usage information will be invaluable and save vast amounts of time if you forget what options are needed...

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