10-601 Decision Trees

From Cohen Courses
Jump to navigationJump to search

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.