Predict from a `brulee_logistic_reg`

## Usage

```
# S3 method for brulee_logistic_reg
predict(object, new_data, type = NULL, epoch = NULL, ...)
```

## Arguments

- object
A

`brulee_logistic_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()) {
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(sex ~ ., data = penguins_train) %>%
step_dummy(all_nominal_predictors()) %>%
step_normalize(all_numeric_predictors())
set.seed(3)
fit <- brulee_logistic_reg(rec, data = penguins_train, epochs = 5)
fit
predict(fit, penguins_test)
predict(fit, penguins_test, type = "prob") %>%
bind_cols(penguins_test) %>%
roc_curve(sex, .pred_female) %>%
autoplot()
}
# }
```