Predictions of a pruned Random Planted Forest

The prediction of a pruned forest is alpha + sum(beta_S * f_S(x)) over the kept components f_S of the forest, with the intercept alpha and the coefficients beta_S (see prune_components()). For classification these are the scores, one per score column, which type turns into probabilities or classes as predict.rpf() does.

Usage

# S3 method for class 'rpf_pruned'
predict(
  object,
  new_data,
  type = ifelse(object$fit$mode == "regression", "numeric", "prob"),
  nthreads = NULL,
  ...
)

Arguments

object

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

new_data

[data.frame | matrix]: New observations to predict.

type

[character(1)]: As in predict.rpf(): "numeric" for regression (the default); for classification "prob" (the default), "class", or "numeric" / "link" (the scores alpha + sum(beta_S * f_S(x)); with loss = "logit" or "exponential" and more than two classes one per class, summing to 0 across the classes).

nthreads

[integer(1) | NULL: NULL]: Number of threads. NULL uses the nthreads the forest was fitted with.

...

Not currently used, but required for extensibility. Unknown arguments are an error.

Value

A tbl with the same number of rows as new_data: for regression column .pred; for classification the columns of predict.rpf() for type.

See also

prune_components(), predict_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)
pruned <- prune_components(fit, x, y, rule = "R", tau = 0.01)
predict(pruned, x[1:5, ])
#> # A tibble: 5 × 1
#>     .pred
#>     <dbl>
#> 1 -0.192 
#> 2 -1.73  
#> 3 -0.0109
#> 4  0.610 
#> 5 -1.45