Additional TreeNet Features
CART®, MARS®, TreeNet®, and Neural Networks
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:
- This definitive classification tree was developed by world-renowned statisticians, including Doctors Jerome Friedman and Leo Breiman. CART is one of the most well-known data mining algorithms and is designed for both non-technical and technical users.
- Ideal for users who prefer results in a form similar to traditional regression while capturing essential non–linearities and interactions.
- TreeNet is Salford's most flexible and powerful data mining tool capable of consistently generating extremely accurate models. It has been responsible for the majority of modeling competition awards and demonstrates remarkable performance. The regression classification algorithm typically generates thousands of small decision trees built in a sequential error correcting process to converge a model.
- Random Forests®:
- Random Forests's features include prediction, clusters and segment discoveries, anomaly tagging detection and multivariate class description. The method was developed by Leo Breiman and Adele Cutler, both of the University of California, Berkeley.
New Components & Features available in version 8.0!
- Generalized Path Seeker is Jerry Friedman's approach to regularized regression. This technology offers high-speed lasso for extreme data set configurations with upwards of 100,000 predictors and possibly very few rows. Such sets are commonplace in gene research and text mining. This is both supremely fast and efficient.
- RuleLearner is a powerful post–processing technique that selects the most influential subset of nodes, thus reducing model complexity. RuleLearner allows the modeler to take advantage of the increased accuracy of very complicated TreeNet and Random Forests models, while still yielding the simplicity of CART models.
TreeNet Price Quote
Additional TreeNet Features are available in Pro, ProEx, and Ultra.
TreeNet System Requirements
TreeNet University Program
The TreeNet® 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.
- Predictive Power:
- TreeNet is Salford's most flexible and powerful data mining tool, capable of consistently generating extremely accurate models. TreeNet’s level of accuracy is usually not attainable by single models or by ensembles such as bagging or conventional boosting. TreeNet demonstrates remarkable performance for both regression and classification. The algorithm typically generates thousands of small decision trees built in a sequential error–correcting process to converge to an accurate model. Tree Net has been responsible for the majority of Salford’s modeling competition awards.
- Supreme Accuracy:
- TreeNet's robustness extends to data contaminated with erroneous target labels. This type of data error can be very challenging for conventional data mining methods and will be catastrophic for conventional boosting. In contrast, TreeNet is generally immune to such errors as it dynamically rejects training data points too much at variance with the existing model. In addition, TreeNet adds the advantage of a degree of accuracy usually not attainable by a single model or by ensembles such as bagging or conventional boosting. As opposed to neural networks, TreeNet is not sensitive to data errors and needs no time-consuming data preparation, preprocessing or imputation of missing values.
- Advanced Features:
- Interaction detection establishes whether interactions of any kind are needed in a predictive model, and is a search engine to discover specifically which interactions are required. The interaction detection system not only helps improve model performance (sometimes dramatically) but also assists in the discovery of valuable new segments and previously unrecognized patterns.
Technical Articles by Jerome Friedman are also available for download:
- Greedy Function Approximation: A Gradient Boosting Machine introduces the methodology.
- Stochastic Gradient Boosting discusses several improvements to the original idea.
SPM® 8 Product Versions
- The best of the best. For the modeler who must have access to leading edge technology available and fastest run times including major advances in ensemble modeling, interaction detection and automation. ULTRA also provides advance access to new features as they become available in frequent upgrades.
- For the modeler who needs cutting-edge data mining technology, including extensive automation of workflows typical for experienced data analysts and dozens of extensions to the Salford data mining engines.
- A true predictive modeling workbench designed for the professional data miner. Variety of supporting conventional statistical modeling tools, programming language, reporting services, and a modest selection of workflow automation options.
- Literally the basics. Salford Systems award winning data mining engines without extensions or automation or surrounding statistical services, programming language, and sophisticated reporting. Designed for small budgets while still delivering our world famous engines