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Machine Learning and AI: Advanced Decision Trees with SPSS

1h 23mAdvanced2024-02-07

Authors

Keith McCormick

Data Miner, Trainer, Speaker, Author

Course details

If you're working towards an understanding of machine learning, it's important to know how to work with decision trees. In this course, explore advanced concepts and details of decision tree algorithms. Learn about the QUEST algorithm and how it handles nominal variables, ordinal and continuous variables, and missing data. Explore the C5.0 algorithm and review some of its key features such as global pruning and winnowing. Plus, dive into a few advanced topics that apply to all decision trees, such as boosting and bagging.

Topics include:
Understanding QUEST functions and applications
C5.0 concepts and practical applications
Understanding information gain
Random forests
Boosting and bagging
Costs and priors

Skills covered

SPSS StatisticsSPSSIBMDecision-MakingMachine LearningArtificial Intelligence (AI)Professional DevelopmentLeadership and ManagementDeep Dive (X:Y)

Concepts

0. Introduction

01 - Advanced decision trees with SPSS
02 - What you should know
03 - Using the exercise files

1. Understanding QUEST

04 - Overview
05 - How QUEST handles nominal variables
06 - How QUEST handles ordinal and continuous variables
07 - How QUEST handles missing data
08 - Pruning in QUEST
09 - Stopping rules in QUEST

2. Understanding C5.0

10 - ID3 and C4.5
11 - Winnowing attributes
12 - Rule sets
13 - Understanding information gain
14 - Pruning in C5.0
15 - How C5.0 handles missing data

3. Advanced Topics

16 - Ensembles
17 - What is bagging
18 - Using bagging for feature selection
19 - Random forests
20 - What is boosting
21 - What is XGBoost
22 - XGBoost Tree node
23 - Costs and priors
24 - XGBoost Linear

Conclusion

25 - Next steps