What's New in Salford Predictive Modeler 8.2/8.3 Main Improvements 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 RandomForests (Bagging Trees) RandomForests regression; Saving out-of-bag scores; Speed enhancements High-Dimensional Multivariate Pattern Discovery Battery Target is now available to identify mutual dependencies in the data Automation (Batteries) 56 pre-packaged scenarios based on years of high-end consulting Hotspot Detection Segment Extraction (Battery Priors) Interaction Detection Missing Value Handling and Imputation Model Assessment and Selection Unified reporting of various performance measures across different models Model Translation SAS, C, Java, PMML, Classic, + Java Data Access (all popular statistical formats supported) Updated Stat Transfer Drivers including R workspaces Model Scoring Score Ensemble (combines multiple models into a powerful predictive machine)
