- 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
- Large Data Handling:
64 bit support; Large memory capacity limited only by your hardware