TreeNet® advantages include:

• Automatic selection from thousands of candidate predictors
  • No prior variable selection or data reduction is required.
• Ability to handle data without preprocessing
  • Data do not need to be rescaled, transformed, or modified in any way.
• Resistance to outliers in predictors or the target variable
• Automatic handling of missing values
• General robustness to dirty and partially inaccurate data
• High Speed
• Trees are grown quickly; small trees are grown extraordinarily quickly.
• TreeNet® is able to focus on the data that are not easily predictable as the model evolves.
  • Thus, as additional trees are grown, fewer and fewer data needs to be processed.
  • In many cases, TreeNet® is able to train effectively on 20% of the data.
• Resistance to Overtraining
  • When working with large data bases, even models with 2,000 trees show little evidence of overtraining.
  • Most models show maximum accuracy well before 1,000 trees are grown.

TreeNet® 's robustness extends to data contaminated with erroneous target labels. For example, in medicine there is some risk that patients labeled as healthy are in fact ill and vice versa. 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. Independent real world tests in text mining, fraud detection, and credit worthiness have shown TreeNet® to be dramatically more accurate on test data than other competing methods.

Of course no one method can be best for all problems in all contexts. Typically, if TreeNet® is not well suited for a problem it will yield accuracies on par with that achievable with a single CART® tree.