Machine Learning and AI Foundations: Decision Trees with SPSS

سرفصل های دوره

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.

01 - Introduction

01 - Welcome

02 - What you should know

03 - Using the exercise files

02 - 1. Decision Trees in IBM SPSS Modeler

01 - Decision tree options in SPSS Modeler

02 - Building a quick CHAID model

03 - Adding a second model with C&RT

04 - Analysis nodes

05 - Lift and gains chart

03 - 2. Understanding CHAID

01 - What is an algorithm

02 - Chi-squared overview

03 - Buliding a tree interactively

04 - Bonferonni adjustment

05 - What is level of measurement

06 - How CHAID handles nominal variables

07 - How CHAID handles ordinal variables

08 - How CHAID handles continuous variables

09 - A quick look at the complete CHAID tree

04 - 3. Understanding C&RT

01 - What is the Gini coefficient

02 - How does C&RT weigh purity and balance

03 - How C&RT handles nominal, ordinal, and continuous variables

04 - How C&RT handles missing data

05 - Understanding pruning

06 - A quick look at the complete C&RT tree

05 - 4. Improving Your Model

01 - Stopping rules in CHAID and C&RT

02 - Exhaustive CHAID

03 - The Auto Classifier tuning trick

06 - Conclusion

01 - Next steps

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تولید کننده: LinkedIn Learning (Lynda)