Predictions for several numbers of components

multi_predict() returns the predictions of a fitted lwpls() model for several values of num_comp at once. All values are obtained from a single pass over the data.

Usage

# S3 method for class '`_lwpls_fit`'
multi_predict(object, new_data, type = NULL, num_comp = NULL, ...)

Arguments

object

A parsnip::model_fit object created from a lwpls() specification.

new_data

A rectangular data object, such as a data frame.

type

A single character value or NULL. Possible values are "numeric", "class", or "prob". When NULL, "numeric" is used for regression and "class" for classification.

num_comp

An integer vector with the numbers of components. Defaults to the value used to fit the model.

...

Not currently used.

Value

A tibble with the same number of rows as new_data and a list column .pred of tibbles, each with a num_comp column and the prediction column(s).

Examples

fit <-
  lwpls(num_comp = 4) |>
  parsnip::set_mode("regression") |>
  parsnip::fit(mpg ~ ., data = mtcars[-(1:5), ])

preds <- parsnip::multi_predict(fit, mtcars[1:5, ], num_comp = 1:4)
preds
#> # A tibble: 5 × 1
#>   .pred           
#>   <list>          
#> 1 <tibble [4 × 2]>
#> 2 <tibble [4 × 2]>
#> 3 <tibble [4 × 2]>
#> 4 <tibble [4 × 2]>
#> 5 <tibble [4 × 2]>
preds$.pred[[1]]
#> # A tibble: 4 × 2
#>   num_comp .pred
#>      <int> <dbl>
#> 1        1  21.5
#> 2        2  22.1
#> 3        3  22.3
#> 4        4  22.1