Implements the SISAL algorithm by Tikka and Hollmén. It is a sequential backward selection algorithm which uses a linear model in a cross-validation setting. Starting from the full model, one variable at a time is removed based on the regression coefficients. From this set of models, a parsimonious (sparse) model is found by choosing the model with the smallest number of variables among those models where the validation error is smaller than a threshold. Also implements extensions which explore larger parts of the search space and/or use ridge regression instead of ordinary least squares.
|Author||Mikko Korpela [aut, cre]|
|Date of publication||2015-10-10 15:45:32|
|Maintainer||Mikko Korpela <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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