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Mastering Machine Learning with R, Second Edition - Second Edition

You're reading from  Mastering Machine Learning with R, Second Edition - Second Edition

Product type Book
Published in Apr 2017
Publisher Packt
ISBN-13 9781787287471
Pages 420 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (23) Chapters close

Title Page
Credits
About the Author
About the Reviewers
Packt Upsell
Customer Feedback
Preface
1. A Process for Success 2. Linear Regression - The Blocking and Tackling of Machine Learning 3. Logistic Regression and Discriminant Analysis 4. Advanced Feature Selection in Linear Models 5. More Classification Techniques - K-Nearest Neighbors and Support Vector Machines 6. Classification and Regression Trees 7. Neural Networks and Deep Learning 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis, Recommendation Engines, and Sequential Analysis 11. Creating Ensembles and Multiclass Classification 12. Time Series and Causality 13. Text Mining 14. R on the Cloud 15. R Fundamentals 16. Sources

Deployment


If everything is done according to the plan up to this point, it might just come down to flipping a switch and your model goes live. Assuming that this is not the case, here are the tasks for this step:

  1. Deploying the plan.
  2. Monitoring and maintaining the plan.
  3. Producing the final report.
  4. Reviewing the project.

After the deployment and monitoring/maintenance and underway, it is crucial for you and those who will walk in your steps to produce a well-written final report. This report should include a white paper and briefing slide. I have to say that I resisted the drive to put my findings in a white paper as I was an indentured servant to the military's passion for PowerPoint slides. However, slides can and will be used against you, cherry-picked or misrepresented by various parties for their benefit. Trust me, that just doesn't happen with a white paper as it becomes an extension of your findings and beliefs. Use PowerPoint to brief stakeholders, but use that the white paper as the document of record and as a preread, should your organization insist on one. It is my standard procedure to create this white paper in R using knitr and LaTex.

Now for the all-important process review, you may have your own proprietary way of conducting it; but here is what it should cover, whether you conduct it in a formal or informal way:

  • What was the plan?
  • What actually happened?
  • Why did it happen or not happen?
  • What should be sustained in future projects?
  • What should be improved upon in future projects?
  • Create an action plan to ensure sustainment and improvement happen

That concludes the review of the CRISP-DM process, which provides a comprehensive and flexible framework to guarantee the success of your project and make you an agent of change.

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Mastering Machine Learning with R, Second Edition - Second Edition
Published in: Apr 2017 Publisher: Packt ISBN-13: 9781787287471
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