This course provides an overview of how to use IBM SPSS Modeler to predict a target field that describes numeric values. Students will be exposed to rule induction models such as CHAID and C&R Tree. They will also be introduced to traditional statistical models such as Linear Regression. Students are introduced to machine learning models, such as Neural Networks. Business use case examples include: predicting the length of subscription for newspapers, telecommunication, and job length, as well as predicting insurance claim amounts.
IBM SPSS Modeler Analysts who have completed the Introduction to IBM SPSS Modeler and Data Mining course who want to become familiar with the modeling techniques available in IBM SPSS Modeler to predict a continuous target.
1: Introduction to predictive models for continuous targets
Determine the classification model to use
2: Building decision trees interactively
Use the model nugget to score records
3: Building decision trees directly
List one difference between CHAID and C&R Tree
4. Using traditional statistical models
Customize options in the Cox node
5: Using machine learning models
Customize one option in the Neural Net node
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