Please refer to course overview

1: Introduction to predictive models for categorical targets • Identify three modeling objectives • Explain the concept of field measurement level and its implications for selecting a modeling technique • List three types of models to predict categorical targets 2: Building decision trees interactively with CHAID • Explain how CHAID grows decision trees • Build a customized model with CHAID • Evaluate a model by means of accuracy, risk, response and gain • Use the model nugget to score records 3: Building decision trees interactively with C&R Tree and Quest • Explain how C&R Tree grows a tree • Explain how Quest grows a tree • Build a customized model using C&R Tree and Quest • 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 • Customize two options in the Quest node • Customize two options in the C5.0 node • Use the Analysis node and Evaluation node to evaluate and compare models • 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

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