Training

Two-Stage Modeling with Enterprise Miner Software

This Level IV course uses the analytical tools found in Enterprise Miner to introduce effective techniques for building and explaining two-stage models. In many predictive modeling applications, it is often possible to predict not only a response probability but also a response amount. By combining this information in a two-stage model, you can more accurately estimate the profit consequences of a business decision.

Course Benefits

After completing this course, you will be able to

build, evaluate, and deploy effective two stage models
explain two-stage modeling results to a non-statistical audience.

Prerequisites

Before attending this course, you should

have completed the Predictive Modeling Using Enterprise Miner course
have some experience with creating and managing SAS data sets, which you can gain from the SAS Programming I: Essentials course.

It is also recommended that you have completed the Neural Network Modeling course.

Course Content

Review of Basic Predictive Modeling Techniques

creating a predictive model using Enterprise Miner
analytic challenges

Building and Evaluating a Two-Stage Model

component model assessment
combining model predictions
adjusting model predictions
assessing combined models

Improving Two-Stage Model Performance

increasing data partitioning effectiveness
improving input selection

Two Stage Neural Network Models

creating separate neural network models
creating a joint neural network model

Explaining Two-Stage Modeling Results

creating rules-based explanations
creating explanation scorecards

Duration: 2.5 days

CEUs: 1.5

What participants say about the M-series:

"The educational content, exchange of ideas, and intellectual environment I found at the conference exceeded my expectations and confirmed SAS' place as the premier data mining conference in the world."

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"Right time. Right place. Right content."

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"This was a superb environment - one of the smartest conference venues I have experienced (and I have experienced a lot). The talks went into greater depth than the talks at many such meetings. Many of the talks were particularly valuable in shedding light on different application areas of data mining."