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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

Brainpower:
56 Pre-packaged scenarios inspired by how leading model analysts structure their work.
Efficiencies:
Cleverly designed automation to relieve the gruntwork/burden on the analyst, allowing the analyst to focus on the creative aspects of model development.
Sophistication:
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.
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.
Bridging-the-gap
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

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Get In Touch With Us

Contact Us

9685 Via Excelencia, Suite 208, San Diego, CA 92126
Ph: 619-543-8880
Fax: 619-543-8888
info (at) salford-systems (dot) com