Random Planted Forest Predictions

Usage

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

Arguments

object

A fit object of class rpf.

new_data

Data for new observations to predict.

type

"numeric" for regression outcomes, "class" for class predictions or "prob" for probability predictions.

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.

...

Unused.

Value

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

For classification: A tbl with one column for each level in y containing class probabilities if type = "prob". For type = "class", one column .pred with class predictions is returned. For type = "numeric" or "link", one column .pred with raw predictions.

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  20.3
#>  2  20.4
#>  3  25.0
#>  4  20.6
#>  5  17.1
#>  6  18.7
#>  7  15.1
#>  8  23.1
#>  9  23.2
#> 10  19.5
#> # ℹ 22 more rows