Parsnip model specification for pprf.

Creates a model specification for a Projection Pursuit random forest. Use set_engine("ppforest2") to select the ppforest2 engine.

Usage

pp_rand_forest(
  mode = "classification",
  trees = NULL,
  mtry = NULL,
  mtry_prop = NULL,
  penalty = NULL
)

Arguments

mode

A character string for the model type. Either "classification" or "regression".

trees

The number of trees in the forest (maps to size).

mtry

The number of variables to consider at each split (maps to n_vars).

mtry_prop

The proportion of variables to consider at each split (maps to p_vars). An alternative to mtry that expresses the feature subsample as a fraction in (0, 1], tunable via the dials mtry_prop() parameter. Supply mtry or mtry_prop, not both.

penalty

The regularization parameter (maps to lambda).

Value

A parsnip model specification.

See also

pprf for the underlying training function, pp_tree for single trees

Examples

# \donttest{
if (requireNamespace("parsnip", quietly = TRUE)) {
  library(parsnip)
  spec <- pp_rand_forest(trees = 50, mtry = 2) %>% set_engine("ppforest2")
  fit <- spec %>% fit(Species ~ ., data = iris)
  predict(fit, iris)
  predict(fit, iris, type = "prob")
}
#> # A tibble: 150 × 3
#>    .pred_setosa .pred_versicolor .pred_virginica
#>           <dbl>            <dbl>           <dbl>
#>  1            1                0               0
#>  2            1                0               0
#>  3            1                0               0
#>  4            1                0               0
#>  5            1                0               0
#>  6            1                0               0
#>  7            1                0               0
#>  8            1                0               0
#>  9            1                0               0
#> 10            1                0               0
#> # ℹ 140 more rows
# }