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)