Predictive Modeling for Categorical Targets Using IBM SPSS Modeler (v18.1.1) SPVC - 0E0U8G

Beschreibung

This course focuses on using analytical models to predict a categorical field, such as churn, fraud, response to a mailing, pass/fail exams, and machine break-down. Students are 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. Students will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.

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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

Ziel

Please refer to course overview

Anforderungen an die Teilnehmer

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.

Teilnehmer

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).