The TreeNet® modeling engine 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, the TreeNet methodology is not sensitive to data errors and needs no time-consuming data preparation, pre-processing or imputation of missing values. This type of data error can be very challenging for conventional data mining methods and will be catastrophic for conventional boosting. In contrast, the TreeNet model is generally immune to such errors as it dynamically rejects training data points too much at variance with the existing model. The TreeNet modeling engine robustness extends to data contaminated with erroneous target labels.
Interaction detection establishes whether interactions of any kind are needed in a predictive model, and is a search engine discovering 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.
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Our University Program provides the SPM®, CART®, MARS®, TreeNet® , and Random Forests® modeling engines at significantly-reduced licensing fees to the educational community.
70+ pre-packaged scenarios, basically experiments, inspired by how leading model analysts structure their work.