has_multi_predict() tests to see if an object can make multiple
predictions on submodels from the same object. multi_predict_args()
returns the names of the arguments to multi_predict() for this model
(if any).
Usage
has_multi_predict(object, ...)
# Default S3 method
has_multi_predict(object, ...)
# S3 method for class 'model_fit'
has_multi_predict(object, ...)
# S3 method for class 'workflow'
has_multi_predict(object, ...)
multi_predict_args(object, ...)
# Default S3 method
multi_predict_args(object, ...)
# S3 method for class 'model_fit'
multi_predict_args(object, ...)
# S3 method for class 'workflow'
multi_predict_args(object, ...)Value
has_multi_predict() returns single logical value while
multi_predict_args() returns a character vector of argument names (or NA
if none exist).
Examples
lm_model_idea <- linear_reg() |> set_engine("lm")
has_multi_predict(lm_model_idea)
#> [1] FALSE
lm_model_fit <- fit(lm_model_idea, mpg ~ ., data = mtcars)
has_multi_predict(lm_model_fit)
#> [1] FALSE
multi_predict_args(lm_model_fit)
#> [1] NA
library(kknn)
knn_fit <-
nearest_neighbor(mode = "regression", neighbors = 5) |>
set_engine("kknn") |>
fit(mpg ~ ., mtcars)
multi_predict_args(knn_fit)
#> [1] "neighbors"
multi_predict(knn_fit, mtcars[1, -1], neighbors = 1:4)$.pred
#> [[1]]
#> # A tibble: 4 × 2
#> neighbors .pred
#> <int> <dbl>
#> 1 1 21
#> 2 2 21
#> 3 3 20.9
#> 4 4 21.0
#>