Predict from a brulee_multinomial_reg

Predict from a brulee_multinomial_reg

Usage

# S3 method for class 'brulee_multinomial_reg'
predict(object, new_data, type = NULL, epoch = NULL, ...)

Arguments

object

A brulee_multinomial_reg object.

new_data

A data frame or matrix of new predictors.

type

A single character. The type of predictions to generate. Valid options are:

• "class" for hard class predictions

• "prob" for soft class predictions (i.e., class probabilities)

epoch

An integer for the epoch to make predictions. If this value is larger than the maximum number that was fit, a warning is issued and the parameters from the last epoch are used. If left NULL, the epoch associated with the smallest loss is used.

...

Not used, but required for extensibility.

Value

A tibble of predictions. The number of rows in the tibble is guaranteed to be the same as the number of rows in new_data.

Examples

# \donttest{
if (torch::torch_is_installed() & rlang::is_installed(c("recipes", "yardstick", "modeldata"))) {

  library(recipes)
  library(yardstick)

  data(penguins, package = "modeldata")

  penguins <- penguins |> na.omit()

  set.seed(122)
  in_train <- sample(1:nrow(penguins), 200)
  penguins_train <- penguins[ in_train,]
  penguins_test  <- penguins[-in_train,]

  rec <- recipe(island ~ ., data = penguins_train) |>
    step_dummy(species, sex) |>
    step_normalize(all_numeric_predictors())

  set.seed(3)
  fit <- brulee_multinomial_reg(rec, data = penguins_train, epochs = 5)
  fit

  predict(fit, penguins_test) |>
    bind_cols(penguins_test) |>
    conf_mat(island, .pred_class)
}
#>            Truth
#> Prediction  Biscoe Dream Torgersen
#>   Biscoe        50     2         3
#>   Dream         12    38         6
#>   Torgersen      7     8         7
# }