0A0U8G - Predictive Modeling for Categorical Targets Using IBM SPSS Modeler (v18.1.1)

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 CR 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.

Code: 0a0u8g

Duration: 1.0 day

Enquire Now

Start learning today!

Click Hereto customize your Training

Objectives

Please refer to course overview

Content

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 CR Tree and Quest

  • Explain how CR Tree grows a tree
  • Explain how Quest grows a tree
  • Build a customized model using CR Tree and Quest
  • List two differences between CHAID, CR Tree, and Quest

4: Building decision trees directly

  • Customize two options in the CHAID node
  • Customize two options in the CR 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, CR 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

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).

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.

Certification

product-certification
This course is not associated with any Certification.

Course Benefits

product-benefits
  • Career growth
  • Broad Career opportunities
  • Worldwide recognition from leaders
  • Up-to Date technical skills
  • Popular Certification Badges

IBM Popular Courses

1o276g

"IBM OpenPages: Create Standard Reports-Part 5" is a tutorial or instructional module that is likely part of a series on using the IBM OpenPages software. In th

6a302g

"IBM Safer Payments Hands-On Technical Primer Training (V6.3)" is an immersive and practical learning program designed to equip participants with the essential

6a322g

"IBM Safer Payments Hands-On SysOps Training (v6.3)" is a specialized and practical training program offered by IBM. This training focuses on educating System O
Enquire Now
 
 
 
 
moYqll
By clicking "Submit", I agree to the Terms Of Use and Privacy Policy