The Statistica Boosted Trees module is a complete implementation of the method usually referred to as stochastic gradient boosting trees. This is also known as TreeNet (™Salford Systems, Inc.) and MART (™Jerill, Inc.)]. These techniques can be used for regression-type problems (to predict a continuous dependent variable) as well as classification problems (to predict a categorical dependent variable).

The general idea is to compute a sequence of simple trees, where each successive tree is built for the prediction residuals of the preceding tree.

It can be shown that such additive weighted expansions of trees can eventually produce an excellent fit of the predicted values to the observed values, even if the specific nature of the relationships between the predictor variables and the dependent variable of interest is very complex (nonlinear in nature). Hence, the method of gradient boosting - fitting a weighted additive expansion of simple trees - represents a general and powerful machine learning algorithm.

For more information see Friedman, 1999a, b; Hastie, Tibshirani, & Friedman, 2001; Nisbet, R., Elder, J., & Miner, G. 2009

## Recommended Comments

There are no comments to display.