Print a pruned Random Planted Forest

Shows the model: its intercept and its components, each with its importance, the component's share of the variance of the model's prediction on the training rows (components$model_share). How the components were chosen (the forest's families and splits, given or chosen out of bag, the rule and threshold, what was left out, the heat-map forms) follows briefly at the end.

Usage

# S3 method for class 'rpf_pruned'
print(x, n_show = 20, ...)

Arguments

x

[rpf_pruned]: A pruned forest from prune_components() or rpf().

n_show

[integer(1): 20]: The largest number of components to list.

...

Further arguments passed to or from other methods.

Value

Invisibly: x.

See also

prune_components().

Examples

set.seed(1)
x <- matrix(runif(300 * 3, -1, 1), ncol = 3, dimnames = list(NULL, paste0("x", 1:3)))
y <- x[, 1] + 2 * x[, 2] * x[, 3] + rnorm(300, sd = 0.3)
fit <- rpf(x, y, max_interaction = 2, n_families = 10, n_splits = 30, prune = FALSE)
prune_components(fit, x, y, rule = "R", tau = 0.01)
#> ── Random Planted Forest model ─────────────────────────────────────────────────
#> Regression. Prediction = intercept + the sum of 3 components.
#> Intercept: -0.0494822
#> 
#> Components (largest first), importance = share of the variance of the prediction on the training rows:
#>  component importance
#>  x2:x3     51%       
#>  x1        37%       
#>  x3        0.093%    
#> 
#> How it was chosen:
#>   The forest: 10 tree families of 30 splits.
#>   From the forest's 6 nonzero components (purified): those with a share of at least 1% of the forest's Var(prediction), each with one coefficient, fitted out of bag (rule R).
#>   Rule and threshold given.
#>   Left out: 3 components, shares together 1.5% (largest x2, 0.75%).