Difference between revisions of "10-601 Decision Trees"
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=== What You Should Know Afterward === | === 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. | ||
Revision as of 12:39, 15 August 2014
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
Readings
- Mitchell, Chapter 3.
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.
