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 `_multnet`
multi_predict(object, new_data, type = NULL, penalty = NULL, ...)
# S3 method for `_glmnetfit`
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 `_torch_mlp`
multi_predict(object, new_data, type = NULL, epochs = NULL, ...)
# S3 method for `_train.kknn`
multi_predict(object, new_data, type = NULL, neighbors = NULL, ...)
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).
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 training epochs.
An integer vector for the number of nearest neighbors.