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

## Usage

```
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 = transform_log10())
tree_depth(range = c(1L, 15L), trans = NULL)
prune(values = c(TRUE, FALSE))
cost_complexity(range = c(-10, -1), trans = transform_log10())
```

## Arguments

- range
A two-element vector holding the

*defaults*for the smallest and largest possible values, respectively. If a transformation is specified, these values should be in the*transformed units*.- trans
A

`trans`

object from the`scales`

package, such as`scales::transform_log10()`

or`scales::transform_reciprocal()`

. 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 [1e-100, Inf]
#> Range (transformed scale): [-10, 1.5]
tree_depth()
#> Tree Depth (quantitative)
#> Range: [1, 15]
prune()
#> Pruning (qualitative)
#> 2 possible values include:
#> TRUE and FALSE
cost_complexity()
#> Cost-Complexity Parameter (quantitative)
#> Transformer: log-10 [1e-100, Inf]
#> Range (transformed scale): [-10, -1]
```