Comments (2)
Thanks for pointing this out! I just updated the cross-validation module and its documentation (it now includes descriptions of cvmeans and cvsds). You'll need to reinstall L0Learn to get those updates.
If you're using L0 regularization alone, then the index of the optimal lambda can be found using OptimalIndex = which.min(fit$cvmeans)
and the optimal lambda can be then extracted using fit$lambda[OptimalIndex]
. For L0L2/L0L1, cvmeans is a list where cvmeans[[i]] is a sequence of cross-validation errors corresponding to fit$gamma[i]. So which.min(fit$cvmeans[[i]])
can be used to get the optimal index for the ith value of gamma.
I will add an example of cross-validation and how to extract the optimal parameters to the Wiki page soon.
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Update: The Usage Wiki page now has an example showing how to use cross-validation using L0L2/L0L1 penalties.
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Related Issues (18)
- Readme images
- OpenMP not found on MAC OS HOT 2
- Control of lambda0 and the corresponding support size? HOT 2
- Clarification on the solution path HOT 1
- Control of number of lambda values in L0Learn.fit with algorithm = "CDPSI" HOT 3
- L0 and L0L2 results from the CDPS(1) method are confusing HOT 6
- Tuning the parameter, gamma, using the function L0Learn.cvfit HOT 2
- Bug in coef HOT 2
- L1L2 and L0L1L2 HOT 1
- Excessive memory usage in L0Learn.cvfit with large n, small p HOT 1
- Pre-specifying lambda grid does not seem to work HOT 5
- Allowing for positivity or box constraints on fitted coefficients? HOT 2
- Prediction results on test data from L0 is much worse than L0L2 or LASSO HOT 2
- Allow for observation weights to be able to fit e.g. identity link Poisson GLMs (with or without nonnegativity constraints) HOT 2
- Support for sparse covariate matrices? HOT 1
- Setting seed in cvfit should not affect global seed
- Tried installing L0Learn in a jupyter notebook
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