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.
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