In parsnip: A Common API to Modeling and Analysis Functions

By default, when not given a fixed penalty, [h2o::h2o.glm()] uses a heuristic approach to select the optimal value of penalty based on training data. Setting the engine parameter lambda_search to TRUE enables an efficient version of the grid search, see more details at https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/lambda_search.html.

The choice of mixture depends on the engine parameter solver, which is automatically chosen given training data and the specification of other model parameters. When solver is set to 'L-BFGS', mixture defaults to 0 (ridge regression) and 0.5 otherwise.