Predictive Modeling for Categorical Targets Using IBM SPSS Modeler (v18.1.1)

Description

At Sixe Engineering we have been providing official IBM training around the world for over 12 years. Get the best training from our specialists in Europe. We have important discounts and offers for two or more students.

Course details

Target audience

• 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).

Desired Prerequisites:

• Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, reading data files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and a basic knowledge of modeling.

• Prior completion of Introduction to IBM SPSS Modeler and Data Science (v18.1) is recommended.

Instructors

The great majority of the IBM courses we offer are taught directly by our engineers. This is the only way we can guarantee the highest quality. We complement all the training with our own materials and laboratories, based on our experience during the deployments, migrations and courses that we have carried out during all these years.

Added value

Our courses are deeply role oriented. To give an example, the needs for technology mastery are different for developer teams and for the people in charge of deploying and managing the underlying infrastructure. The level of previous experience is also important and we take it very seriously. That is why beyond (boring) commands and tasks, we focus on solving the problems that arise in the day to day of each team. Providing them with the knowledge, competencies and skills required for each project. In addition, our documentation is based on the latest version of each product.

Agenda and course syllabus

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

Do you need to adapt this syllabus to your needs? Are you interested in other courses?  Ask us without obligation.

Locations for on-site delivery

• Austria: Vienna
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• Turkey: Ankara
• United Kingdom: London