A Study of RandomForests Learning Mechanism with Application to the Identification of Informative Gene Interactions in Microarray Data
Additional Random Forests Features are available in Pro, ProEx, and Ultra.
RandomForests is a randomized collection of CART trees designed for predictive accuracy in datasets containing many predictors and a small number of records. RandomForests was developed by Leo Breiman and Adele Cutler of the University of California, Berkeley.
The SPM Salford Predictive Modeler® software suite is a highly accurate and ultra-fast platform for creating predictive, descriptive, and analytical models from databases of any size, complexity, or organization. The SPM® software suite has automation that accelerates the process of model building by conducting substantial portions of the model exploration and refinement process for the analyst. While the analyst is always in full control, we optionally anticipate the analyst's next best steps and package a complete set of results from alternative modeling strategies for easy review. Do in one day what normally requires a week or more using other systems.
The Salford Predictive Modeler® software suite includes:
New Components & Features available in version 8.0!
Random Forests Price Quote
Random Forests Scalability
Random Forests Special Features
Random Forests Supported Filetypes
The RandomForests® data-translation engine supports data conversions for more than 80 file formats, including popular statistical-analysis packages such as SAS® and SPSS®, databases such as Oracle and Informix, and spreadsheets such as Microsoft Excel and Lotus 1-2-3.
Random Forests Systems Requirements
Random Forests University Program
Random Forests Videos