Random Planted Forest Predictions

Usage

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

Arguments

object

[rpf]: A fitted rpf model.

new_data

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

type

[character(1)]: "numeric" for regression outcomes, "class" for class predictions or "prob" for probability predictions. Defaults to "numeric" for regression and "prob" for classification.

For classification and loss = "L1" or "L2", "numeric" yields raw predictions which are not guaranteed to be valid probabilities in [0, 1]. For type = "prob", these are truncated to ensure this property.

If loss is "logit" or "exponential", type = "link" is an alias for type = "numeric", as in this case the raw predictions have the additional interpretation similar to the linear predictor in a glm.

nthreads

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

...

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

Value

For regression: A tbl with column .pred with the same number of rows as new_data.

For classification: A tbl with one column .pred_<level> for each level in y containing class probabilities if type = "prob". For type = "class", one column .pred_class with class predictions is returned. For type = "numeric" or "link", raw predictions are returned: one column .pred for binary outcomes, and one column .pred_<level> per level for multiclass outcomes. With loss = "logit", the first level is the reference class and has no column, so K - 1 columns are returned for K levels.

Examples

# Regression with L2 loss
rpfit <- rpf(y = mtcars$mpg, x = mtcars[, c("cyl", "wt")])
predict(rpfit, mtcars[, c("cyl", "wt")])
#> # A tibble: 32 × 1
#>    .pred
#>    <dbl>
#>  1  20.7
#>  2  20.5
#>  3  25.0
#>  4  21.0
#>  5  17.8
#>  6  18.3
#>  7  14.9
#>  8  23.5
#>  9  22.5
#> 10  18.6
#> # ℹ 22 more rows