# NOT RUN {
# Suppose we were tuning a linear regression model that was fit with glmnet
# and there was a predictor that used a spline basis function to enable a
# nonlinear fit. We can use `penalty()` and `mixture()` for the glmnet parts
# and `deg_free()` for the spline.
# A full 3^3 factorial design where the regularization parameter is on
# the log scale:
simple_set <- grid_regular(penalty(), mixture(), deg_free(), levels = 3)
simple_set
# A random grid of 5 combinations
set.seed(362)
random_set <- grid_random(penalty(), mixture(), deg_free(), size = 5)
random_set
# A small space-filling design based on experimental design methods:
design_set <- grid_max_entropy(penalty(), mixture(), deg_free(), size = 5)
design_set
# }
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