Purify a Random Planted Forest

Purifies an rpf object.

Usage

purify(x, ...)

# Default S3 method
purify(x, ...)

# S3 method for class 'rpf'
purify(x, ..., maxp_interaction = NULL, mode = 2L, nthreads = NULL)

is_purified(x)

Arguments

x

And object of class rpf.

...

(Unused)

maxp_interaction

integer or NULL: Only compute/store purified components up to this interaction order. Higher-order purified trees are zeroed (not computed) but still implicitly influence lower orders during purification. If NULL, purify all orders (default behavior).

mode

integer(1): Purification algorithm mode. 1 = legacy grid path used by fit$fit$purify(); 2 = fast exact KD-tree based path. Defaults to 2.

nthreads

integer or NULL: number of threads to use. If NULL, defaults to min of the object's configured nthreads and available threads.

Value

Invisibly: The rpf object.

Details

Unless rpf() is called with purify = TRUE, the forest has to be purified after fit to ensure the components extracted by predict_components() are valid. predict_components() will automatically purify a forest if is_purified() reports FALSE.

Examples

rpfit <- rpf(mpg ~., data = mtcars, max_interaction = 2, ntrees = 10)
purify(rpfit)
#> -- Regression Random Planted Forest --
#> 
#> Formula: mpg ~ . 
#> Fit using 10 predictors and 2-degree interactions.
#> Forest is purified!
#> 
#> Called with parameters:
#> 
#>              loss: L2
#>            ntrees: 10
#>   max_interaction: 2
#>            splits: 30
#>         split_try: 10
#>             t_try: 0.4
#>  split_decay_rate: 0.1
#>    max_candidates: 50
#>     delete_leaves: TRUE
#>   split_structure: leaves
#>             delta: 0
#>           epsilon: 0.1
#>     deterministic: FALSE
#>          nthreads: 1
#>            purify: FALSE
#>                cv: FALSE