Enterprise Miner Software™

Duration: 3.0 days CEUs: 1.8

AUDIENCE
This course teaches several predictive modeling techniques available in SAS/Enterprise Miner Software. Students learn the Enterprise Miner Interface. Predictive models based on Logistic Regression, Neural Networks and Decision Trees are developed.

BENEFITS
Students will be able to:
• Navigate the Enterprise Miner Interface
• Construct a Process Flow Diagram
• View Distribution Characteristics of Variables
• Transform Input Variables
• Sample and Subset Data
• Incorporate Sub-Diagrams into Process Flow Diagram
• Create HTML Summary Report of EnterpriseMiner Project
• Generate and save SAS code created byEnterprise Miner
• Develop Logistic Regression Model
• Develop Neural Network Multilayer PerceptronModel (MLP)
• Develop Neural Network Cascade Model
• Develop Decision Tree Model
• Use Lift Charts and ROC curves to assessindividual models and compare different models.
• Score data
• Create composite model from several models.

PREREQUISITES
• Programming I: SAS Essentials course orequivalent understanding
• Understand basic statistical concepts
• Understand Linear and Logistic Regression

COURSE TOPICS

Overview
• Concepts and terminology
• Overall Enterprise Miner capabilities
• Interface components

Create Process Flow Diagram
• Adding Nodes
• Opening nodes
• Modifying node parameters
• Running a diagram

Data Preparation
• Select data set
• Assign variable roles
• Partition data set into different model roles
• View variable distribution characteristics
• Transform variables
• Filter outliers
• Imputation techniques to replace missing values

Create Sub diagram
• Select nodes to include in sub-diagram
• Enter and Exit nodes for sub-diagram
• Collapse sub-diagram

Logistic Regression
• Default settings
• Selection criteria
• Setting probabilities for variables to enter and remain in model
• Significant variables
• Parameter estimates
• Misclassification rate and overall model assessment

Neural Networks
• Basic concepts and terminology
• Construct a Multilayer Perceptron Model
• Construct a Neural Network based on Principal Components
• Construct a Cascade Neural Network
• Modifying underlying architecture
• Changing model parameters
• Variable weights
• Training history
• Misclassification rate and overall model assessment

Decision Trees
• Basic concepts and terminology
• Tree structures
• Splitting Criteria
• Pruning
• Misclassification rate and overall model assessment

Score Data
• Score an independent data set
• Save score code to external file
• Use score code outside Enterprise Miner

Assess Model
• Assess individual models
• Compare models
• Lift chart
• ROC curve

Composite Model
• Combine several models to form composite model

Software Used: Base SAS® and ENTERPRISE MINER ® Software.