The package implements a self-designed Boosting algorithm. The procedure is based on two consecutive linear componentwise boosting runs, where features are choosen to minimize the MSE. The features returned after both boosting runs are then used to fit a penalized linear regression model.
|Author||person("Lukas Jan", "Stroemsdoerfer", email = "[email protected]", role = c("aut", "cre"))|
|Maintainer||Lukas Stroemsdoerfer <[email protected]>|
|Package repository||View on GitHub|
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