Difference between revisions of "10-601 Decision Trees"
From Cohen Courses
Jump to navigationJump to search| Line 7: | Line 7: | ||
=== Readings === | === Readings === | ||
| − | * Mitchell, Chapter 3. | + | * Mitchell, Chapter 3 or Murphy 16.1-16.2 |
=== What You Should Know Afterward === | === What You Should Know Afterward === | ||
Latest revision as of 15:58, 6 January 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
- William's lecture: Slides in Powerpoint, in PDF.
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.
