Work toward a mastery of machine learning by exploring advanced decision tree algorithm concepts. Learn about the QUEST and C5.0 algorithms and a few advanced topics.

Syllabus

Introduction

Welcome
What you should know
Using the exercise files

1. Understanding QUEST

Overview
How QUEST handles nominal variables
How QUEST handles ordinal and continuous variables
How QUEST handles missing data
Pruning in QUEST
Stopping rules in QUEST

2. Understanding C5.0

ID3 and C4.5
Winnowing attributes
Rule sets
Understanding information gain
Pruning in C5.0
How C5.0 handles missing data

3. Advanced Topics

Ensembles
What is bagging?
Using bagging for feature selection
Random forests
What is boosting?
Costs and priors

Conclusion

Next steps

Taught by

Keith McCormick

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