Boosted Generalized Additive Models (bgam) package 1.0

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 ordinaryleast squares regression. The GLM generalizes linear regression by allowingthe linear model to be related to the response variable via a link functionand by allowing the magnitude of the variance of each measurement to be afunction of its predicted value. (Wikipedia)A common example of a GLM is binomial-logistic distribution/inverse linkGLM (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 internaldynamics 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 isa 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 gradientof the likelihood with respect to eta. Regularization is usually done by early-stoppingwhere 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-linkcombos include Gaussian/identity, Binomial/Logistic, Poisson/exponential. The package isobject-oriented and new distro-link combos and learners can be implemented and usedwith ease. The package includes facilities for cross-validation, including a parallel implementation through the parallel computing toolbox. It also allows a subsetof 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)References:Friedman, Hastie and Tibshirani. Additive logistic regression: astatistical view of boosting. Ann. Statist. Volume 28, Number 2 (2000),337-407.BdoDshlmann and Hothorn. Boosting Algorithms: Regularization, Predictionand 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 AdditiveModels. Chapman & Hall/CRC.

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Binomiallogistic,  Crossvalidation,  Facilities,  Gaussianidentity,  Implemented,  Include,  Includes,  Objectoriented,  Poissonexponential,  Toolbox
Users rating:	0/10