Machine Learning and AI Foundations: Decision Trees with SPSS

1h 27mBeginner2024-02-02

Authors

Keith McCormick

Data Miner, Trainer, Speaker, Author

Course details

Many data science specialists are looking to pivot toward focusing on machine learning. This course covers the essentials of machine learning, including predictive analytics and working with decision trees. Explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables. Demonstrations of using the IBM SPSS Modeler are included so you can understand how decisions trees work. This course is designed to give you a solid foundation on which to build more advanced data science skills.

Topics include:
Using the SPSS Modeler
Building a CHAID model
Adding a second model with C&RT
Analysis notes
Using a lift and gains chart
Exploring algorithms
Building a tree interactively
The Bonferonni adjustment
Handling nominal, ordinal, and continuous variables
Examining the CHAID tree
The Gini coefficient
Weighing purity and balance
Understanding pruning
Examining the C&RT tree
Applying stopping rules
Using the Auto Classifier tuning trick

Skills covered

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

Concepts

0. Introduction

01 - Decision trees in SPSS Modeler
02 - What you should know
03 - Modeler 18.4
04 - Using the exercise files

1. Decision Trees in IBM SPSS Modeler

05 - Decision tree options in SPSS Modeler
06 - Building a quick CHAID model
07 - Adding a second model with C&RT
08 - Analysis nodes
09 - Lift and gains chart

2. Understanding CHAID

10 - What is an algorithm
11 - Chi-squared overview
12 - Buliding a tree interactively
13 - Bonferonni adjustment
14 - What is level of measurement
15 - How CHAID handles nominal variables
16 - How CHAID handles ordinal variables
17 - How CHAID handles continuous variables
18 - A quick look at the complete CHAID tree

3. Understanding C&RT

19 - What is the Gini coefficient
20 - How does C&RT weigh purity and balance
21 - How C&RT handles nominal, ordinal, and continuous variables
22 - How C&RT handles missing data
23 - Understanding pruning
24 - A quick look at the complete C&RT tree

4. Improving Your Model

25 - Stopping rules in CHAID and C&RT
26 - Exhaustive CHAID
27 - Tree-AS node
28 - The Autoclassifier tuning trick

Conclusion

29 - Next steps