GLMNet Learners

Heuristics provides learners for training GLMNet models, which we describe on this page along with a guide to their parameters.

Shared Parameters

All of learners provided by Heuristics for training GLMNet models are GLMNetLearners. In addition to the shared learner parameters, these learners support all parameters of GLMNet.jl under the same names, with some additional remarks:

  • the GLMNet parameter rng is not used, and randomness should instead be controlled using the general learner parameter random_seed
  • the GLMNet parameter weight is not used, and weights should instead be set using the sample_weight keyword argument as for other learners
  • the GLMNet parameter nfolds is not used, and weights should instead be set using the n_folds keyword argument

Classification Learners

The GLMNetCVClassifier is used for training GLMNet models for classification problems with cross-validation. The following values for criterion are permitted:

There are no additional parameters beyond the shared parameters.

Regression Learners

The GLMNetCVRegressor is used for training GLMNet models for regression problems with cross-validation. The following values for criterion are permitted:

In addition to the shared parameters, these learners also support the shared regression parameters.

Survival Learners

The GLMNetCVSurvivalLearner is used for training GLMNet models for survival problems. The following values for criterion are permitted:

In addition to the shared parameters, these learners also support the shared survival parameters.