bgam - Boosted Generalized Additive Models package --- Implements boosting for the Generalized Additive and Linear Models (GAM and GLM). Extensible, fully documented. Implements linear and stub learners, least-squares/logistic/Poisson regression. The generalized linear model (GLM) is a flexible generalization of ordinary least squares regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value. (Wikipedia) A common example of a GLM is binomial-logistic distribution/inverse link GLM (aka logistic regression), where: eta = X*w, y ~ Binomial( logistic (eta )) This GLM allows one to tackle classification problems (where the output is 0 or 1) in a quasi-linear way. The generalized additive model (GAM) is a generalization of the GLM where the internal dynamics are nonlinear, but nevertheless additive: eta_i = f_1(X^(i,1)) + f_2(X^(i,2)) + ... f_i are known as smoothers or (in the context of boosting) as learners. Boosting is a method of fitting GAMs and by extension GLMs by building up a model (eta) iteratively, by, at every iteration, adding to the model the learner most similar to the gradient of the likelihood with respect to eta. Regularization is usually done by early-stopping where the optimal number of iterations is determined through validation. bgam is a well-documented package that implements boosting with GAMs. It currently implements linear learners and stubs (depth-1 trees). Implemented distro-link combos include Gaussian/identity, Binomial/Logistic, Poisson/exponential. The package is object-oriented and new distro-link combos and learners can be implemented and used with ease. The package includes facilities for cross-validation, including a parallel implementation supporting the parallel computing toolbox. It also allows a subset of the data to be used at any boosting iteration (stochastic gradient boosting). Open up TestBgam.m in the editor for several usage examples. Contributions and requests for new features are welcome. Author: Patrick Mineault (patrick DOT mineault AT gmail DOT com) History: 02/07/2011 - 1.2.0 - Parallel cross-validation added 04/03/2011 - 1.1.0 - Added .mex file for StubLearner Tweaked cross-validation code 24/01/2011 - 1.0.1 - Removed temporary .m~ files from .zip 23/01/2011 - 1.0.0 - Initial release References: Friedman, Hastie and Tibshirani. Additive logistic regression: a statistical view of boosting. Ann. Statist. Volume 28, Number 2 (2000), 337-407. Bühlmann and Hothorn. Boosting Algorithms: Regularization, Prediction and Model Fitting. Statist. Sci. Volume 22, Number 4 (2007), 477-505. Wood. Generalized Additive Models: an introduction with R. CRC Press, 2006. Hastie, T. J. and Tibshirani, R. J. (1990). Generalized Additive Models. Chapman & Hall/CRC.