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MART (Multiple Additive Regression Trees) is an implementation of the gradient tree boosting methods for predictive data mining (regression and classification). For a detailed description of the methodology and applications, use the references below. For a quick overview of the methodology and implementation, follow the first resource link. MART has widespread usage in Medicine, Biology, Marketing and Finance.

**MART REFERENCES
**

Friedman, J. H. (2001), Greedy function approximation: the gradient boosting machine, Annals of Statistics.

Friedman, J. H. (1999), Stochastic gradient boosting, Technical report, Stanford University.

Friedman, J. H. & Fisher, N. (1999), Bump hunting in high dimensional data, Statistics and Computing 9, pp. 123-143.

Friedman, J. H. & Meulman J. J. (2003), Multiple additive regression trees with application in epidemiology, Statistics in Medicine, Vol. 22, Issue 9, pp. 1365-1381.

Hastie, T., Tibshirani, R., & Friedman, J. H. (2008). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.

Freund, Y. & Schapire, R. (1997), A decision-theoretic generalization of online learning and an application to boosting, Journal of Computer and System Sciences 55, pp. 119-139.

Buhlmann, P. & Hothorn, T. (2008), Boosting algorithms: Regularization, prediction and model Fitting, Statistical Science.

- MART: Overview, Tutorial, Data - a web-resource by the author of MART Jerome Friedman
- Jerome Friedman's main page

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