These are parameter generating functions that can be used for modeling, especially in conjunction with the parsnip package.

trees(range = c(1L, 2000L), trans = NULL)

min_n(range = c(2L, 40L), trans = NULL)

sample_size(range = c(unknown(), unknown()), trans = NULL)

sample_prop(range = c(1/10, 1), trans = NULL)

loss_reduction(range = c(-10, 1.5), trans = log10_trans())

tree_depth(range = c(1L, 15L), trans = NULL)

prune(values = c(TRUE, FALSE))

cost_complexity(range = c(-10, -1), trans = log10_trans())

Arguments

range

A two-element vector holding the defaults for the smallest and largest possible values, respectively.

trans

A trans object from the scales package, such as scales::log10_trans() or scales::reciprocal_trans(). If not provided, the default is used which matches the units used in range. If no transformation, NULL.

values

A vector of possible values (TRUE or FALSE).

Details

These functions generate parameters that are useful when the model is based on trees or rules.

  • trees(): The number of trees contained in a random forest or boosted ensemble. In the latter case, this is equal to the number of boosting iterations. (See parsnip::rand_forest() and parsnip::boost_tree()).

  • min_n(): The minimum number of data points in a node that is required for the node to be split further. (See parsnip::rand_forest() and parsnip::boost_tree()).

  • sample_size(): The size of the data set used for modeling within an iteration of the modeling algorithm, such as stochastic gradient boosting. (See parsnip::boost_tree()).

  • sample_prop(): The same as sample_size() but as a proportion of the total sample.

  • loss_reduction(): The reduction in the loss function required to split further. (See parsnip::boost_tree()). This corresponds to gamma in xgboost.

  • tree_depth(): The maximum depth of the tree (i.e. number of splits). (See parsnip::boost_tree()).

  • prune(): A logical for whether a tree or set of rules should be pruned.

  • cost_complexity(): The cost-complexity parameter in classical CART models.

Examples

trees()
#> # Trees (quantitative) #> Range: [1, 2000]
min_n()
#> Minimal Node Size (quantitative) #> Range: [2, 40]
sample_size()
#> # Observations Sampled (quantitative) #> Range: [?, ?]
loss_reduction()
#> Minimum Loss Reduction (quantitative) #> Transformer: log-10 #> Range (transformed scale): [-10, 1.5]
tree_depth()
#> Tree Depth (quantitative) #> Range: [1, 15]
prune()
#> Pruning (qualitative) #> 2 possible value include: #> TRUE and FALSE
cost_complexity()
#> Cost-Complexity Parameter (quantitative) #> Transformer: log-10 #> Range (transformed scale): [-10, -1]