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I originally started writing this notebook to serve as an introduction decision trees. It's a description of the "1-rule" algorithm which I think is worth studying for the following reasons:

  • It’s arguably the simplest and most useful machine learning algorithm you can learn
  • It’s a simple introduction to “decision trees”
  • It’s a simple introduction to “information entropy“
  • It has minimal mathematical content, so that anyone technical can follow the ideas

I first encountered the “1 Rule” prediction algorithm in ‘Data Mining Practical Machine Learning Tools and Techniques’ by ‘Ian H. Witton’. A book which serves as a great introduction to machine learning ideas.

Content of the Notebook

  • If you had to choose just one feature?
  • If you didn't have any features?
  • Information Entropy
  • Reducing Disorder
  • Feature Selection
  • Going Further

You can access the notebook here

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Comment by Dan Steinberg on January 7, 2019 at 11:06am

It is IAN WITTEN -- you have a typo in his name

Comment by Vincent Granville on January 5, 2019 at 7:32am

The link was working when initially posted. This one is working:

https://github.com/Smeths/1RulePrediction/blob/master/1RulePredicti...

But the original one provided a nice summary.  

Comment by Mustafa Koc on January 4, 2019 at 3:41pm

Hi,

links are not working for notebooks.

Thanks

MKOC

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