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.