Model Compression and Rule Extraction: Unified reporting of various performance measures
Linear Methods for Regression, Recent Advances and Discoveries: OLS Regression
Regularized Regression Including: LAR/LASSO Regression, Ridge Regression, Elastic Net Regression
Linear Methods for Classification, Recent Advances and Discoveries: LOGIT • LAR/LASSO • Ridge • Elastic Net/ Generalized Path Seeker
Outlier Detection: GUI reports, tables, and graphs
Time Series Modeling
Data Preparation: Battery Bin for automatic binning of a user selected set of variables with large number of options
Model Simplification Methods: ISLE • RuleLearner
Ensemble Learning: Battery Bootstrap • Battery Model
Unsupervised Learning: Breiman's Column Scrambler
Parallel Processing: Automatic support of multiple cores via multithreading
Large Data Handling: 64 bit support • Large memory capacity limited only by your hardware
Minitab Connection: prepared for interaction with Minitab
Other Improvements: Ever expanding stream of additions and modifications to our core tools, based on user feedback and new levels of understanding of our flagship products.
Further Improvements
CART Classification and Regression Trees
User defined linear combination lists for splitting; Constrains on trees; Automatic addition of missing value indicators; Enhanced GUI reporting; User controlled Cross Validation; Out-of-bag performance stats and predictions; Profiling terminals nodes based on user supplied variables; Comparison of Train vs. Test consistency across nodes; RandomForests-style variable importance.
MARS (Automated Nonlinear Regression)
Updated GUI interface; Model performance based on independent test sample or Cross Validation; Support for time series models
TreeNet (Gradient Boosting, Boosted Trees)
One-Tree TreeNet (CART alternative); RandomForests via TreeNet (RandomForests regression alternative) Interaction Control Language (ICL); Interaction strength reporting; Enhanced partial dependency plots; RandomForests-style randomized splits
