Prediction wrappers for h2o — h2o

Prediction wrappers for fitted models with h2o engine that include data conversion, h2o server cleanup, and so on.

Usage

h2o_predict(object, new_data, ...)

h2o_predict_classification(object, new_data, type = "class", ...)

h2o_predict_regression(object, new_data, type = "numeric", ...)

# S3 method for `_H2OAutoML`
predict(object, new_data, id = NULL, ...)

Arguments

object: An object of class model_fit
new_data: A rectangular data object, such as a data frame.
...: Other options passed to h2o::h2o.predict()
type: A single character value or NULL. Possible values are "numeric", "class", "prob", "conf_int", "pred_int", "quantile", "time", "hazard", "survival", or "raw". When NULL, predict() will choose an appropriate value based on the model's mode.
id: Model id in AutoML results.

Value

For type != "raw", a prediction data frame with the same number of rows as new_data. For type == "raw", return the result of h2o::h2o.predict().

Details

For AutoML, prediction is based on the best performing model.

Examples

if (h2o_running()) {
  spec <-
    rand_forest(mtry = 3, trees = 100) %>%
    set_engine("h2o") %>%
    set_mode("regression")

  set.seed(1)
  mod <- fit(spec, mpg ~ ., data = mtcars)
  h2o_predict_regression(mod$fit, new_data = head(mtcars), type = "numeric")

  # using parsnip
  predict(mod, new_data = head(mtcars))
}