This course (formerly Classifying Customers Using IBM SPSS Modeler) focuses on analytical models to predict a categorical field (churn, fraud, response to a mailing, pass/fail exams, machine break-down, and so forth). Students will be introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. The student will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.
Analytics business users who have completed the Introduction to IBM SPSS Modeler and Data Mining course and who want to become familiar with analytical models to predict a categorical field (yes/no churn, yes/no fraud, yes/no response to a mailing, pass/fail exams, yes/no machine break-down, and so forth).
Please refer to course overview.
Experience using IBM SPSS Modeler, including familiarity with the IBM SPSS Modeler environment, creating streams, importing data (Var. File node), basic data preparation (Type node, Derive node, Select node), reporting (Table node, Data Audit node), and creation of models.
Prior completion of Introduction to Predictive Modeling Using IBM SPSS Modeler (v18) (0D007) is recommended.
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
If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. It's a cost effective option.
Submit an enquiry from any page on this site, and let us know you are interested in the requirements box, or simply mention it when we contact you.