Predict using TabICL2

Predict using TabICL2

Usage

# S3 method for class 'tab_icl_v2'
predict(object, new_data, ...)

# S3 method for class 'tab_icl_v2.regressor'
predict(object, new_data, levels, train_dim, ...)

# S3 method for class 'tab_icl_v2.classifier'
predict(object, new_data, levels, train_dim, ...)

# S3 method for class 'tab_icl_v2'
augment(x, new_data, ...)

Arguments

object

A tab_icl_v2 object.

new_data

A data frame or matrix of new predictors.

...

Not used, but required for extensibility.

levels

for regressor, the number of quantiles. For classifier, the number of levels in the outcome.

train_dim

the number of rows of the training part within new_data.

x

A tab_icl_v2 object.

Value

predict() returns a tibble of predictions and augment() appends the columns in new_data. In either case, the number of rows in the tibble is guaranteed to be the same as the number of rows in new_data.

For regression data, the prediction is in the column .pred. For classification, the class predictions are in .pred_class and the probability estimates are in columns with the pattern .pred_{level} where level is the levels of the outcome factor vector.

Examples

car_split <- rsample::initial_split(mtcars)

if (FALSE) { # \dontrun{
# Fit
if (torch_is_installed() & interactive()) {
 mod <- tab_icl2(mpg ~ cyl + log(drat), car_split)

 # Predict
 predict(mod, testing(car_split))

 # Augment
 augment(mod, testing(car_split))
}
} # }