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Mastering Machine Learning with R

You're reading from   Mastering Machine Learning with R Master machine learning techniques with R to deliver insights for complex projects

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781783984527
Length 400 pages
Edition 1st Edition
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Table of Contents (15) Chapters Close

Preface 1. A Process for Success FREE CHAPTER 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 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis and Recommendation Engines 11. Time Series and Causality 12. Text Mining A. R Fundamentals Index

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 of this step:

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

After the deployment and monitoring/maintenance is underway, it is crucial for yourself and those that 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.

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 did 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 happens

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.

You have been reading a chapter from
Mastering Machine Learning with R
Published in: Oct 2015
Publisher:
ISBN-13: 9781783984527
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