Difference between revisions of "10-601 Decision Trees"

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(Created page with "This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2013 === Slides === * [http://www.cs.cmu.edu/~wcohen/10-601/classification-and-decision-trees.ppt...")
 
 
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This a lecture used in the [[Syllabus for Machine Learning 10-601 in Fall 2013]]
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This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]]
  
 
=== Slides ===
 
=== Slides ===
  
* [http://www.cs.cmu.edu/~wcohen/10-601/classification-and-decision-trees.pptx Slides in Powerpoint].
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* William's lecture: [http://www.cs.cmu.edu/~wcohen/10-601/decision-trees.pptx Slides in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-601/decision-trees.pdf in PDF].
  
 
=== Readings ===
 
=== Readings ===
  
* Mitchell, Chapter 3.
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* Mitchell, Chapter 3 or Murphy 16.1-16.2
  
 
=== What You Should Know Afterward ===
 
=== What You Should Know Afterward ===
  
* TBD
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* What a decision tree is, and how to classify an instance using a decision tree.
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* What the canonical top-down algorithm is for learning a decision tree.
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** What heuristics are used for choosing a decision-tree split.
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** What entropy is, and what information gain is.
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* What reduced-error pruning is, and why it might improve classification performance.
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* Some of the advantages and disadvantages of decision-tree learning in specific, and eager learning in general, compared to K-NN learning.

Latest revision as of 16:58, 6 January 2016

This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016

Slides

Readings

  • Mitchell, Chapter 3 or Murphy 16.1-16.2

What You Should Know Afterward

  • What a decision tree is, and how to classify an instance using a decision tree.
  • What the canonical top-down algorithm is for learning a decision tree.
    • What heuristics are used for choosing a decision-tree split.
    • What entropy is, and what information gain is.
  • What reduced-error pruning is, and why it might improve classification performance.
  • Some of the advantages and disadvantages of decision-tree learning in specific, and eager learning in general, compared to K-NN learning.