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    By Phone or Online

    Access the help you need to use our software from representatives who are knwoledgeable in data mining and predictive analytics

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What's New

56 Pre-packaged scenarios inspired by how leading model analysts structure their work.
Cleverly designed automation to relieve the gruntwork/burden on the analyst, allowing the analyst to focus on the creative aspects of model development.
Advanced Algorithms not found anywhere else.
Enhanced Regression:
Regression and Logistic Regression vastly enhanced to incorporate the key concepts of modern data mining approaches specifically geared toward massive datasets.
Ever expanding stream of additions and modifications to our core tools, based on user feedback and new levels of understanding of our flagship products.
between advances in academic thinking pioneered by Jerome Friedman and real-world applications.

Improvements to Existing Features and Components

  • 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)

New Algorithms and Features Specific to SPM® v8.2

  • Unsupervised Learning
    Breiman’s Column Scrambler
  • Model Compression and Rule Extraction:
    Unified reporting of various performance measures
  • Parallel Processing:
    Automatic support of multiple cores via multithreading
  • Outlier Detection:
    GUI reports, tables, and graphs
  • 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
  • Ensemble Learning:
    Battery Bootstrap; Battery Model
  • 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
  • Large Data Handling:
    64 bit support; Large memory capacity limited only by your hardware


Get In Touch With Us

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Ph: 619-543-8880
9685 Via Excelencia, Suite 208, San Diego, CA 92126