It contains links to the paper as well as documentation/slides.
Tips
LassoNet sometimes require fine tuning. For optimal performance, consider
making sure that the initial dense model (with ) has trained well, before starting the LassoNet regularization path. This may involve hyper-parameter tuning, choosing the right optimizer, and so on. If the dense model is underperforming, it is likely that the sparser models will as well.
making sure the stepsize over the path is not too large. By default, the stepsize runs over the logscale between two values and .
) has trained well, before starting the LassoNet regularization path. This may involve hyper-parameter tuning, choosing the right optimizer, and so on. If the dense model is underperforming, it is likely that the sparser models will as well.
path is not too large. By default, the stepsize runs over the logscale between two values
and