For some models, predictions can be made on sub-models in the model object.
multi_predict(object, ...)# S3 method for default
multi_predict(object, ...)
# S3 method for `_xgb.Booster`
multi_predict(object, new_data, type = NULL, trees = NULL, ...)
# S3 method for `_C5.0`
multi_predict(object, new_data, type = NULL, trees = NULL, ...)
# S3 method for `_elnet`
multi_predict(object, new_data, type = NULL, penalty = NULL, ...)
# S3 method for `_lognet`
multi_predict(object, new_data, type = NULL, penalty = NULL, ...)
# S3 method for `_earth`
multi_predict(object, new_data, type = NULL, num_terms = NULL, ...)
# S3 method for `_multnet`
multi_predict(object, new_data, type = NULL, penalty = NULL, ...)
# S3 method for `_train.kknn`
multi_predict(object, new_data, type = NULL, neighbors = NULL, ...)
A model_fit
object.
Optional arguments to pass to predict.model_fit(type = "raw")
such as type
.
A rectangular data object, such as a data frame.
A single character value or NULL
. Possible values
are "numeric", "class", "prob", "conf_int", "pred_int", "quantile",
or "raw". When NULL
, predict()
will choose an appropriate value
based on the model's mode.
An integer vector for the number of trees in the ensemble.
A numeric vector of penalty values.
An integer vector for the number of MARS terms to retain.
An integer vector for the number of nearest neighbors.
A tibble with the same number of rows as the data being predicted.
There is a list-column named .pred
that contains tibbles with
multiple rows per sub-model. Note that, within the tibbles, the column names
follow the usual standard based on prediction type
(i.e. .pred_class
for
type = "class"
and so on).