Machine Learning & AI Foundations: Decision Trees

Machine Learning & AI Foundations: Decision Trees

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Course Description

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.

What you will learn

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


Section 1: Introduction

Section 2: 1. Decision Trees in IBM SPSS Modeler

Section 3: 2. Understanding CHAID

Section 4: 3. Understanding C&RT

Section 5: 4. Improving Your Model

Section 6: Conclusion