Machine Learning,
Decision Trees, Overfitting
Machine Learning 10-701
Tom M. Mitchell
Center for Automated Learning and Discovery
Carnegie Mellon University
September 13, 2005
Recommended reading: Mitchell, Chapter 3
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Various Decision trees and overfitting, Summaries of Machine Learning
Decision trees and overfitting
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Machine Learning,
Decision Trees, Overfitting
Machine Learning 10-
Tom M. Mitchell
Center for Automated Learning and Discovery
Carnegie Mellon University
September 13, 2005
Recommended reading
Mitchell, Chapter 3
Machine Learning:
Study of algorithms that• improve their performance• at some task• with experience
Object Detection
Example training images
for each orientation
(Prof. H. Schneiderman)
Text Classification
Company home page
vs Personal home page
vs Univeristy home page
vs …
Growth of Machine Learning
Machine learning is preferred approach to
- Speech recognition, Natural language processing– Computer vision– Medical outcomes analysis– Robot control– …
This trend is accelerating
- Improved machine learning algorithms– Improved data capture, networking, faster computers– Software too complex to write by hand– New sensors / IO devices– Demand for self-customization to user, environment
Decision tree learning
node
= Root
[ID3, C4.5, …]
Sample Entropy