The mtry
argument denotes the number of predictors that will be randomly sampled at each split when creating tree models.
Some engines, such as "xgboost"
, "xrf"
, and "lightgbm"
, interpret their analogue to the mtry
argument as the proportion of predictors that will be randomly sampled at each split rather than the count. In some settings, such as when tuning over preprocessors that influence the number of predictors, this parameterization is quite helpful---interpreting mtry
as a proportion means that [0, 1]
is always a valid range for that parameter, regardless of input data.
parsnip and its extensions accommodate this parameterization using the counts
argument: a logical indicating whether mtry
should be interpreted as the number of predictors that will be randomly sampled at each split. TRUE
indicates that mtry
will be interpreted in its sense as a count, FALSE
indicates that the argument will be interpreted in its sense as a proportion.
mtry
is a main model argument for \code{\link[=boost_tree]{boost_tree()}} and \code{\link[=rand_forest]{rand_forest()}}, and thus should not have an engine-specific interface. So, regardless of engine, counts
defaults to TRUE
. For engines that support the proportion interpretation (currently "xgboost"
and "xrf"
, via the rules package, and "lightgbm"
via the bonsai package) the user can pass the counts = FALSE
argument to set_engine()
to supply mtry
values within [0, 1]
.
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