Frequently Asked Questions for TreeNet

TreeNet® is Salford’s most flexible and powerful data mining tool, responsible for at least a dozen prizes in major data mining competitions since its introduction in 2002. The algorithm typically generates thousands of small decision trees built in a sequential error–correcting process to converge to an accurate model.

TreeNet models are usually complex and thus the software generates a number of special reports designed to extract the meaning of the model. Graphs produced by TreeNet software display the impact of any relevant predictor or pair of predictors on the target, thus revealing the underlying data structure.

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.