There is a new version of this course. Please see Risk Modeling Add-on for SAS Visual Machine Learning: Using Credit Scoring Nodes (multi-version course) (+10 virtual lab hours).

This course provides an overview of the Risk Modeling nodes in SAS Visual Data Mining and Machine Learning. It prepares members of your organization to develop scorecards. The course includes hands-on demonstrations and teaches you key concepts, terminology, and base functionality that are integral to visual risk modeling.

Ismerje meg hogyan... use SAS Visual Data Mining and Machine Learning to:

• Add data to the pipeline.
• Insert an Interactive Grouping node into the pipeline and specify grouping options.
• Add a Scorecard node to generate a scorecard.
• Control the selection and optimization option of the logistic regression model.
• Manually adjust bins and scorecard cutoff values.
• Insert a Reject Inference node into the pipeline.

Kinek ajánljuk

Modelers, credit analysts, and others who are interested in risk modeling

Before attending this course, you should have completed the Supervised Machine Learning Pipelines Using SAS Viya course. Other prerequisites are knowledge of and experience with logistic regression, which you can gain by completing the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression and Forecasting Using Model Studio in SAS Viya courses.

This course addresses SAS Risk Modeling Add-on for SAS Visual Data Mining and Machine Learning in SAS Viya 3.5. This course also touches on SAS Visual Analytics software.

Introduction

• Define credit risk.
• Define scorecards.
• Understand the risk modeling workflow.

Interactive Grouping Node

• Understand the purpose of grouping.
• Identify measures used to determine the overall predictive power of a characteristic.
• Add an Interactive Grouping node to the pipeline.
• Manage bins interactively.

Scorecard Node

• Understand basic logistic regression selection methods.
• Add a Scorecard node to the pipeline.
• Manage scorecard points and ranges interactively.

Reject Inference Node

Define the reason for using a Reject Inference node.
• Add a Reject Inference node to the pipeline.