Data preparation

Includes interactive data preparation tools that make it easy to apply required data transformations, derive new variables and run intelligent feature selection methods, such as variable selection based on trees and random forests.   

Customizable model templates

Provides out-of-the-box model building templates that can be customized and shared across projects and users.

Self-service machine learning techniques

Includes linear regression, logistic regression, decision trees, random forests, generalized linear models, gradient boosting, neural networks, Bayesian network and support vector machines.

Champion model identification

Uses a variety of interactive, customizable assessment techniques to automatically select the champion model for each segment.

Model exception identification

Provides standardized, easy-to-understand reports that pinpoint issues with models and identify the best models with high confidence. You can then easily recognize underperforming models.   

Model retraining

Lets you retrain models over time using new data and variables, including REST endpoints.

Scalable processing

Runs analytical procedures on a single machine, via grid computing or in-memory processing.

Flexible model deployment

Lets you deploy models in database or in Hadoop to score new data using SAS Scoring Accelerator.

 Add-on to SAS^® Enterprise Miner^™

SAS Factory Miner runs as an add-on to SAS Enterprise Miner.