SAS^® Factory Miner

Data access and preparation

• Access to data sources (including SAS data sets, database tables, and Hadoop files) that are registered in SAS Metadata Server
• Interactive assignment of data source metadata (such as variable roles, levels, and order) or use of automated settings to share variable settings across projects
• Data segmentation for stratified modeling
• Automated data profiling and interactive variable distribution graphs for detecting data issues
• Data filtering
• Data transformations
• Data cleaning with statistical and machine learning imputation methods

Customizable supervised learning templates

Ability to interactively build custom templates that include models and the following processing steps:

• Filtering
• Principal components
• Imputation
• Transformations
• Supervised and unsupervised variable selection
• Create your own model templates
• Edit any data preparation or model parameters and save as customized template
• Ability to share model templates across projects and users

Self-service machine learning techniques

Build models that use the following techniques:

• Bayesian networks
• Decision trees
• Gradient boosting
• Neural networks
• Random forests
• Support vector machines
• Generalized linear models
• Linear regression
• Logistic regression
• Interactive visualization of model-specific results

Champion model identification

• Automatic identification for each segment by using selectable criteria
• Manual overrides of system-selected models
• Interactive comparison and assessment of models within a segment and across multiple segments

Model performance exception identification

• Reports that highlight model performance exceptions to enable easy 
• Identification of underperforming models
• Detail drill-down

Model tracking and reporting

• Summary reports that contain model results, significant variables, and model settings
• Reports in PDF and RTF for easy sharing

Model retraining

• Retraining of existing model templates on new data sets
• Tracking of assessment statistics across retraining iterations
• Longitudinal model performance degradation reports

Flexible model management and deployment

• Automatic generation of SAS score code for all model templates
• Registration of models to SAS Model Manager for centralized model deployment and management (requires SAS Model Manager)
• Model deployment in database and in Hadoop using SAS Scoring Accelerator (requires SAS Scoring Accelerator)

Scalable processing for training models

• Multithreaded procedures on SAS servers to take advantage of multicore servers
• Asynchronous processes via SAS Grid Manager for workload balancing and scheduling (requires SAS Grid Manager)
• In memory by using SAS High-Performance Data Mining on database appliances -such as Oracle, Teradata, Greenplum, and SAP HANA - or on Hadoop (requires SAS High-Performance Data Mining)