Additional Random Forests Features are available in Pro, ProEx, and Ultra.
The following videos cover the underlying methods in the SPM® 8.2 Software Suite and provide demonstrations of each of the modeling engines.
Software Featured in the Videos:
- SPM® 8.2 Software Suite
- CART® Software
- RandomForests® Software
- TreeNet® Software
- MARS® Software
- RuleLearner™ Software
- ISLE© Software
- GeneralizedPathSeeker™ Software
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
Breiman and Cutler’s Random Forests®:
Random Forests modeling engine is a collection of many CART® trees that are not influenced by each other when constructed. The sum of the predictions made from decision trees determines the overall prediction of the forest. Random Forests' strengths are spotting outliers and anomalies in data, displaying proximity clusters, predicting future outcomes, identifying important predictors, discovering data patterns, replacing missing values with imputations, and providing insightful graphics.
Cluster and Segment:
Much of the insight provided by the Random Forests modeling engine is generated by methods applied after the trees are grown and include new technology for identifying clusters or segments in data as well as new methods for ranking the importance of variables. The method was developed by Leo Breiman and Adele Cutler of the University of California, Berkeley, and is licensed exclusively to Minitab.
Suited for Wide Datasets:
Random Forests is a collection of many CART trees that are not influenced by each other when constructed. The sum of the predictions made from decision trees determines the overall prediction of the forest. Random Forests is best suited for the analysis of complex data structures embedded in small to moderate data sets containing less than 10,000 rows but potentially millions of columns.
Feature Matrix and download PDF
This guide provides an introduction into RandomForests Modeling Basics.
Introduction to SPM® 8.2 Software & Exploring Data
A Fast Introduction to RandomForests® Software
CART® Software For Regression: Part I
Introduction to MARS® Software for Regression
Introduction to TreeNet® Software for Binary Classification
Scoring New Data (Generate Predictions)