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

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.