This paper discusses several new methods available in Credit Scoring for SAS Enterprise Miner that help build scorecards that are based on interval targets.
The Credit Scoring add-on in SAS Enterprise Miner is widely used to build binary target (good, bad) scorecards for probability of default. The process involves grouping variables using weight of evidence, and then performing logistic regression to produce predicted probabilities. This paper will demonstrate how to use the same tools to build binned variable scorecards for Loss Given Default, explaining the theoretical principles behind the method and use actual data to demonstrate how it was done.
This paper benchmarks SAS and open-source products to analyze big data by modeling four classification problems from real customers. The products that were benchmarked are SAS Rapid Predictive Modeler (a component of SAS Enterprise Miner), SAS High-Performance Analytics Server (using Hadoop), R and Apache Mahout. Results were compared in terms of model quality, modeler effort, scalability and completeness.
This paper shows how to find a profitable customer group (ones likely to buy or respond positively to marketing campaigns when they are targeted)and how to maximize return on investment by using SAS Enterprise Miner.
This paper briefly explains the theory of survival analysis and provides an introduction to its implementation in SAS Enterprise Miner.
This paper discusses the options and methods available for use in High- Performance Data Mining and uses real data for performance benchmarks.
This paper introduces high-performance text mining technology for SAS High- Performance Analytics.
The ever increasing need to process greater amounts of unstructured text data in less time to improve predictive modeling are addressed with high performance enablement of text mining methods. This paper describes the new high-performance procedures that can be used to decrease the time needed for your text mining tasks.
This paper first teaches you how to write pattern-matching rules in SAS Enterprise Content Categorization and then shows you how to apply these patterns as a parsing step in SAS Text Miner.
This paper discusses how two SAS technologies – Text Analytics and Content Categorization Suite -- were used to generate comprehensive and dynamic categories and clusters of the entire corpus of SAS user presentations from inception to the present.
This paper demonstrates how a business analyst in a call center environment can identify emerging topics, generate automatic rules for those topics, edit and refine those rules to improve results, derive insights through visualization, and deploy the resulting model to score new data.
This paper presents the results of experiments with applying Sabermetrics-style principles to the game of cricket and correlating the findings with analysis of opinionated text.