Detailed Course Outline
1. Introduction to predictive modeling for categorical targets • Identify three modeling objectives • List three types of models to predict categorical targets • Explain the concept of field measurement level and its implications for selecting a modeling technique 2. Building decision trees interactively with CHAID • Explain how CHAID grows decision trees • Build a customized model with CHAID • Use the model nugget to score records • Evaluate a model by means of accuracy, risk, response and gain 3. Building decision trees interactively with C&R Tree and Quest • Explain how C&R Tree grows a tree • Build a customized model using C&R Tree and Quest • Explain how Quest grows a tree • List two differences between CHAID, C&R Tree, and Quest 4. Building decision trees directly • Customize two options in the CHAID node • Customize two options in the C&R Tree node • Use the Analysis node and Evaluation node to evaluate and compare models • Customize two options in the Quest node • Customize two options in the C5.0 node • List two differences between CHAID, C&R Tree, Quest, and C5.0 5. Using traditional statistical models • Explain key concepts for Discriminant • Customize one option in the Discriminant node • Explain key concepts for Logistic • Customize one option in the Logistic node 6. Using machine learning models • Explain key concepts for Neural Net • Customize one option in the Neural Net node