R/aaa_multi_predict.R
, R/boost_tree.R
, R/linear_reg.R
, and 4 more
multi_predict.Rd
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, ...)
object | A |
---|---|
... | Optional arguments to pass to |
new_data | A rectangular data object, such as a data frame. |
type | A single character value or |
trees | An integer vector for the number of trees in the ensemble. |
penalty | A numeric vector of penalty values. |
num_terms | An integer vector for the number of MARS terms to retain. |
neighbors | 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).