chapter 3: sas rapid predictive modeler

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Chapter 3: SAS Rapid Predictive Modeler

3.1 Introduction

3.2 SAS Rapid Predictive Modeler Process Overview

3.3 SAS Rapid Predictive Modeler Model Settings

3.4 SAS Rapid Predictive Modeler Output

3.5 Saving Model Project Data

3.6 Registering the Model

3.7 Scoring

3.8 SAS Rapid Predictive Modeler Methods in SAS Enterprise Miner

3.9 Opening SAS Rapid Predictive Modeler Diagrams in SAS Enterprise Miner

3.10 Modifying SAS Rapid Predictive Modeler Diagrams

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Chapter 3: SAS Rapid Predictive Modeler

3.1 Introduction 3.1 Introduction 3.2 SAS Rapid Predictive Modeler Process Overview

3.3 SAS Rapid Predictive Modeler Model Settings

3.4 SAS Rapid Predictive Modeler Output

3.5 Saving Model Project Data

3.6 Registering the Model

3.7 Scoring

3.8 SAS Rapid Predictive Modeler Methods in SAS Enterprise Miner

3.9 Opening SAS Rapid Predictive Modeler Diagrams in SAS Enterprise Miner

3.10 Modifying SAS Rapid Predictive Modeler Diagrams

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Objectives Present a typical approach to data mining. State the key business drivers of SAS Rapid

Predictive Modeler. Present an alternative approach to data mining where

the business analyst and subject matter expert develops his or her own models.

Describe the key capabilities of SAS Rapid Predictive Modeler.

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Churn Case StudyAnalysis Goal:

A telecommunications company wants to decrease the number of churning customers through the development of a churn classification model.

Data set: CHURN_RPM

Number of rows: 4,708

Number of columns: 15

Contents: account information, call history, equipment and complaint history

Targets: TARGET_CHURN (binary)

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Churn Case Study: BasicsThroughout this chapter, you work with data in SAS Enterprise Guide and SAS Enterprise Miner to perform fast and accurate modeling with SAS Rapid Predictive Modeler.

1. Import the CHURN_RPM data.

2. Build the SAS Rapid Predictive Modeler model in SAS Enterprise Guide.

3. Score the CHURN_RPM_SCORE data set.

4. Open the model in SAS Enterprise Miner.

5. Improve the model.

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Business Analyst / Subject

Matter Expert

Database Admin / IT

Quantitative Modeler /

StatisticianModel Development, Deployment, and ManagementSAS Enterprise Miner, SAS/STAT

Data preparation and data cleansingSAS Data Integration Studio

Apply model to specific customer issues (ex. find out customers, which are most likely to churn)SAS Enterprise Guide, SAS Add-In for Microsoft Office

Data Mining and Predictive AnalyticsConventional Approach

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SAS Rapid Predictive ModelerKey Business Drivers

Need to generate numerous models to support a variety of business problems.

Models need to be developed in a short time-frame using a self-service approach.

Does not have to always rely on a statistician or modeler.

Collaborate to augment, validate, and deploy models.

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Generate predictive models in a quick, automated fashionEasy-to-understand reports and chartsRegister model in SAS metadataSAS Enterprise Guide or SAS Add-In for Microsoft Office

Refine model and perform model comparisonTest, validate, and select champion model Monitor model performance for degradationSAS Enterprise Miner SAS, Model Manager

Business Analyst / Subject

Matter Expert

Database Admin / IT

Quantitative Modeler /

Statistician

Data preparation and data cleansingSAS Enterprise Data Integration Server

SAS Rapid Predictive ModelerComplementary Approach

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SAS Rapid Predictive ModelerPrimary Objectives

Generate predictive models quickly and accurately.

Provide self-sufficiency to business users.

Generate easy-to-understand charts and reports.

Integrate analytics and BI for better decisions.

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SAS Rapid Predictive Modeler’s Target Customers Across all industries Those dealing with customer-oriented and

marketing-analytics-oriented issues Those who need to generate numerous models to support a variety of

business problems:– customer acquisition– up-sell and cross-sell– customer retention– customer churn

Business analysts, subject matter experts, and business professionals with little to no statistical knowledge

Statisticians or data miners who need to develop quick baseline models that address common business issues

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What Is SAS Rapid Predictive Modeler? SAS Rapid Predictive Modeler is a customized task that

runs prebuilt SAS Enterprise Miner models. It is an add-in for SAS Enterprise Guide or

SAS Add-In for Microsoft Office. It requires SAS Enterprise Miner and is included in SAS

Enterprise Miner packaging.– It also works with SAS Enterprise Miner for Desktop.

It enables business users, without prior statistical knowledge, to build predictive models quickly and effectively.

Results can be consumed in simple and easy-to-understand charts to make better decisions.

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What Is SAS Rapid Predictive Modeler?

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Key Capabilities You choose from basic, intermediate, or advanced prebuilt methods. SAS Rapid Predictive Modeler automatically handles outliers,

missing values, rare target events, skewed data, variable selection, and model selection.

Analytic results are presented in easy-to-understand business terms: scorecard, lift charts, and listing of key variables in the model.

Analytic experts can further customize and improve models developed in SAS Rapid Predictive Modeler using SAS Enterprise Miner.

Models are registered in SAS metadata to– automate the execution of score code – ease deployment to other systems.

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Chapter 3: SAS Rapid Predictive Modeler

3.1 Introduction

3.2 SAS Rapid Predictive Modeler Process 3.2 SAS Rapid Predictive Modeler Process OverviewOverview

3.3 SAS Rapid Predictive Modeler Model Settings

3.4 SAS Rapid Predictive Modeler Output

3.5 Saving Model Project Data

3.6 Registering the Model

3.7 Scoring3.8 SAS Rapid Predictive Modeler Methods in SAS

Enterprise Miner3.9 Opening SAS Rapid Predictive Modeler Diagrams in

SAS Enterprise Miner

3.10 Modifying SAS Rapid Predictive Modeler Diagrams

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Objectives Give an overview of the SAS Rapid Predictive Modeler

process.

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SAS Rapid Predictive Modeler Modeling Process: Overview1. Open SAS Enterprise Guide or Microsoft Excel.

2. Invoke the SAS Rapid Predictive Modeler task.

3. Select the data to model.

4. Define modeling roles (done automatically if variables are aptly named – for example, target_churn).

5. Run.

6. Review results. (You can save and share them.)

7. (optional) Save task to a SAS Enterprise Miner project.

8. (optional) Register the model in SAS metadata.

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Open SAS Enterprise Guide

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Invoke SAS Rapid Predictive ModelerTasks Data Mining Rapid Predictive Modeler

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Select the Data for Modeling

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Define Modeling Roles

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Run

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Review Results

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Invoking and Running the SAS Rapid Predictive Modeler Task

Churn Case Study

Task: Invoke and execute the Rapid Predictive Modeler task in SAS Enterprise Guide.

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Chapter 3: SAS Rapid Predictive Modeler

3.1 Introduction

3.2 SAS Rapid Predictive Modeler Process Overview

3.3 SAS Rapid Predictive Modeler Model Settings3.3 SAS Rapid Predictive Modeler Model Settings3.4 SAS Rapid Predictive Modeler Output

3.5 Saving Model Project Data

3.6 Registering the Model

3.7 Scoring

3.8 SAS Rapid Predictive Modeler Methods in SAS Enterprise Miner

3.9 Opening SAS Rapid Predictive Modeler Diagrams in SAS Enterprise Miner

3.10 Modifying SAS Rapid Predictive Modeler Diagrams

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Objectives Give a high-level overview of the SAS Rapid

Predictive Modeler model settings.

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SAS Rapid Predictive Modeler: Data PanelAssociate input variables with modeling roles.

Required: dependent

variable (target)

Optional: Set frequency

count. Set ID. Exclude input

variables. Edit data

and filter.

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SAS Rapid Predictive Modeler: Model PanelSpecify the complexity level of the model to build.

Default: Basic

Other methods: Intermediate Advanced

Other selections: Decisions and

priors

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SAS Rapid Predictive Modeler: Model PanelDecisions and Priors

Event level

Prior probabilities

Decision function

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SAS Rapid Predictive Modeler: Report PanelSelect additional features to be included in the model summary report.

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Chapter 3: SAS Rapid Predictive Modeler

3.1 Introduction

3.2 SAS Rapid Predictive Modeler Process Overview

3.3 SAS Rapid Predictive Modeler Model Settings

3.4 SAS Rapid Predictive Modeler Output3.4 SAS Rapid Predictive Modeler Output3.5 Saving Model Project Data

3.6 Registering the Model

3.7 Scoring

3.8 SAS Rapid Predictive Modeler Methods in SAS Enterprise Miner

3.9 Opening SAS Rapid Predictive Modeler Diagrams in SAS Enterprise Miner

3.10 Modifying SAS Rapid Predictive Modeler Diagrams

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Objectives Review the charts and reports generated as output by

SAS Rapid Predictive Modeler.

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SAS Rapid Predictive Modeler: Standard Report OutputModel Gains Chart

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SAS Rapid Predictive Modeler: Standard Report OutputROC Chart

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SAS Rapid Predictive Modeler: Standard Report OutputScorecard

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SAS Rapid Predictive Modeler: Standard Report OutputProject Information

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SAS Rapid Predictive Modeler: Optional Report Output Model Summarization

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SAS Rapid Predictive Modeler: Optional Report Output Variable Ranking

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SAS Rapid Predictive Modeler: Optional Report Output Crosstabulations

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SAS Rapid Predictive Modeler: Optional Report Output Classification Matrix

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SAS Rapid Predictive Modeler: Optional Report Output Fit Statistics

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SAS Rapid Predictive Modeler: Optional Report Output Cumulative Lift Plot

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SAS Rapid Predictive Modeler: Optional Report Output Model Comparison*

* Only available with intermediate or advanced methods

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Chapter 3: SAS Rapid Predictive Modeler

3.1 Introduction

3.2 SAS Rapid Predictive Modeler Process Overview

3.3 SAS Rapid Predictive Modeler Model Settings

3.4 SAS Rapid Predictive Modeler Output

3.5 Saving Model Project Data3.5 Saving Model Project Data3.6 Registering the Model

3.7 Scoring

3.8 SAS Rapid Predictive Modeler Methods in SAS Enterprise Miner

3.9 Opening SAS Rapid Predictive Modeler Diagrams in SAS Enterprise Miner

3.10 Modifying SAS Rapid Predictive Modeler Diagrams

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Objectives Demonstrate how SAS Enterprise Miner project data

from an RPM model can be saved for later inspection and refinement.

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SAS Rapid Predictive Modeler: Options PanelSave SAS Enterprise Miner project data from your SAS Rapid Predictive Modeler model.

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Chapter 3: SAS Rapid Predictive Modeler

3.1 Introduction

3.2 SAS Rapid Predictive Modeler Process Overview

3.3 SAS Rapid Predictive Modeler Model Settings

3.4 SAS Rapid Predictive Modeler Output

3.5 Saving Model Project Data

3.6 Registering the Model3.6 Registering the Model3.7 Scoring

3.8 SAS Rapid Predictive Modeler Methods in SAS Enterprise Miner

3.9 Opening SAS Rapid Predictive Modeler Diagrams in SAS Enterprise Miner

3.10 Modifying SAS Rapid Predictive Modeler Diagrams

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Objectives Show how a SAS Rapid Predictive Modeler model can

be registered to the SAS Metadata Repository and explain why this might be necessary.

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Register the SAS Rapid Predictive Modeler ModelRegister the model to the SAS Metadata Repository.

Use Cases: Import and score using the

Model Scoring task in SAS Enterprise Guide.

Import into SAS Enterprise Miner using the Model Import node for integrated model comparison.

Import into SAS Model Manager for champion/challenger model management.

Import into SAS Data Integration Studio to score with mining results transformation.

Publish as a scoring function for Teradata, Netezza, or IBM DB2.

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Chapter 3: SAS Rapid Predictive Modeler

3.1 Introduction

3.2 SAS Rapid Predictive Modeler Process Overview

3.3 SAS Rapid Predictive Modeler Model Settings

3.4 SAS Rapid Predictive Modeler Output

3.5 Saving Model Project Data

3.6 Registering the Model

3.7 Scoring3.7 Scoring3.8 SAS Rapid Predictive Modeler Methods in SAS

Enterprise Miner3.9 Opening SAS Rapid Predictive Modeler Diagrams in

SAS Enterprise Miner

3.10 Modifying SAS Rapid Predictive Modeler Diagrams

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Objectives Demonstrate how a new data set can be scored with

SAS Rapid Predictive Modeler. Discuss the steps of the model scoring task.

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Model Scoring with SAS Rapid Predictive ModelerTasks Data Mining Model Scoring

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Verify Data

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Select Scoring Model

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Select Scoring Model

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Map Variables

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Select Output

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Save Output Data

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Confirm and Finish

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Scoring Results

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Scoring Review Score new data using the Model Scoring task in

SAS Enterprise Guide. Score new data with SAS Enterprise Miner.

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Chapter 3: SAS Rapid Predictive Modeler

3.1 Introduction

3.2 SAS Rapid Predictive Modeler Process Overview

3.3 SAS Rapid Predictive Modeler Model Settings

3.4 SAS Rapid Predictive Modeler Output

3.5 Saving Model Project Data

3.6 Registering the Model

3.7 Scoring

3.8 SAS Rapid Predictive Modeler Methods in 3.8 SAS Rapid Predictive Modeler Methods in SAS Enterprise MinerSAS Enterprise Miner

3.9 Opening SAS Rapid Predictive Modeler Diagrams in SAS Enterprise Miner

3.10 Modifying SAS Rapid Predictive Modeler Diagrams

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Objectives Discuss the basic, intermediate, and

advanced methods. Show how these methods translate to

SAS Enterprise Miner diagrams.

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Basic Method: Eight Nodes

Samples only if a rare target event Decision tree for variable selection Forward stepwise regression model

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Intermediate Method: 18 Nodes

Builds onto the basic method Several variable selection techniques performed Multiple variable transformations Decision tree and regression models used Variable interactions considered

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Advanced Method: 32 Nodes

Builds onto the intermediate method Includes neural network, advanced regression, and

ensemble models

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Chapter 3: SAS Rapid Predictive Modeler

3.1 Introduction

3.2 SAS Rapid Predictive Modeler Process Overview

3.3 SAS Rapid Predictive Modeler Model Settings

3.4 SAS Rapid Predictive Modeler Output

3.5 Saving Model Project Data

3.6 Registering the Model

3.7 Scoring

3.8 SAS Rapid Predictive Modeler Methods in SAS Enterprise Miner

3.9 Opening SAS Rapid Predictive Modeler 3.9 Opening SAS Rapid Predictive Modeler Diagrams in SAS Enterprise MinerDiagrams in SAS Enterprise Miner

3.10 Modifying SAS Rapid Predictive Modeler Diagrams

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Objectives Demonstrate how a SAS Rapid Predictive Modeler

diagram can be opened in SAS Enterprise Miner.

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Opening the SAS Rapid Predictive Modeler Project in SAS Enterprise Miner1. Open SAS Enterprise Miner.

2. Select New Project.

3. Point to the folder where the SAS Rapid Predictive Modeler project resides.

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Opening the SAS Rapid Predictive Modeler Project in SAS Enterprise MinerSelect Yes for the Project Exist prompt.

Provide the location of the SAS metadata folder.

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SAS Rapid Predictive Modeler Project Opened in SAS Enterprise Miner

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Chapter 3: SAS Rapid Predictive Modeler

3.1 Introduction

3.2 SAS Rapid Predictive Modeler Process Overview

3.3 SAS Rapid Predictive Modeler Model Settings

3.4 SAS Rapid Predictive Modeler Output

3.5 Saving Model Project Data

3.6 Registering the Model

3.7 Scoring3.8 SAS Rapid Predictive Modeler Methods in SAS

Enterprise Miner3.9 Opening SAS Rapid Predictive Modeler Diagrams in

SAS Enterprise Miner

3.10 Modifying SAS Rapid Predictive Modeler 3.10 Modifying SAS Rapid Predictive Modeler DiagramsDiagrams

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Objectives Demonstrate how a SAS Rapid Predictive Modeler

diagram can be modified in SAS Enterprise Miner. Demonstrate SAS Rapid Predictive Modeler on a

census income data set.

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Changing the Default Settings

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Changing the Default Settings

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Changing the Default Settings

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Changing the Default Settings

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Changing the Default Settings

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Census Income Case StudyAnalysis Goal:

The goal is to develop a model to predict whether a person makes more than 50K a year based on census data.

Data set: ADULT

Number of rows: 32,562

Number of columns: 14

Contents: age, work class, education, marital status, race, sex,

capital gain or loss,hours worked, and so on

Targets: TARGET (binary)

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Census Income Case Study: BasicsThis case study demonstrates how to invoke SAS Rapid Predictive Modeler from SAS Enterprise Guide and then open the generated project in SAS Enterprise Miner.

1. Import the ADULT data.

2. Build the SAS Rapid Predictive Modeler model in SAS Enterprise Guide.

3. Open the model in SAS Enterprise Miner.

4. Inspect the results.

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Invoking and Running the SAS Rapid Predictive Modeler Task

Census Income Case Study

Task: Given a set of attributes, use the Rapid Predictive Modeler task to determine whether a person makes more than 50K a year.

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Exercise

This exercise reinforces the concepts discussed previously.

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ResourcesSAS Global Forum 2010 Paper by Wayne Thompson and David Duling support.sas.com/resources/papers/proceedings10/113-2010.pdf

UCI Machine Learning Repository Frank, A. and A. Asuncion. 2010. UCI Machine Learning Repository

[archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.