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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.
Citation | sisal citation info |
github.com/mvkorpel/sisal | |
Copyright | Aalto University |
Bug report | File report |
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