Decision Tree Learning

This talk is based on

• Tom M. Mitchell. Machine Learning. McGraw Hill. 1997. Chapter 3.
• and his slides.

1. 1 Introduction
   1. 1.1 Uses
2. 2 Representation
3. 3 When to Consider Decision Trees
4. 4 Building a Decision Tree
   1. 4.1 Choosing the Best Attribute
   2. 4.2 Entropy
   3. 4.3 Entropy as Encoding Length
   4. 4.4 Non Boolean Entropy
   5. 4.5 Information Gain
   6. 4.6 ID3
   7. 4.7 ID3 Example
5. 5 Hypothesis Space Search by ID3
6. 6 Inductive Bias
   1. 6.1 Restriction and Preference Biases
   2. 6.2 Occam's Razor
7. 7 Issues in Decision Tree Learning
   1. 7.1 Overfitting
      1. 7.1.1 Dealing With Overfitting
      2. 7.1.2 Reduced-Error Pruning
      3. 7.1.3 Rule Post-Pruning
   2. 7.2 Continuous-Valued Attributes
   3. 7.3 Attributes with Many Values
   4. 7.4 Unknown Attribute Values
   5. 7.5 Attributes With Costs

Entire Presentation with Notes

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Copyright © 2009 José M. Vidal . All rights reserved.

22 January 2003, 07:51AM