Model predictions across many sub-models
For some models, predictions can be made on sub-models in the model object.
multi_predict(object, ...) ## Default S3 method: multi_predict(object, ...) ## S3 method for class ''_xgb.Booster'' multi_predict(object, new_data, type = NULL, trees = NULL, ...) ## S3 method for class ''_C5.0'' multi_predict(object, new_data, type = NULL, trees = NULL, ...) ## S3 method for class ''_elnet'' multi_predict(object, new_data, type = NULL, penalty = NULL, ...) ## S3 method for class ''_lognet'' multi_predict(object, new_data, type = NULL, penalty = NULL, ...) ## S3 method for class ''_earth'' multi_predict(object, new_data, type = NULL, num_terms = NULL, ...) ## S3 method for class ''_multnet'' multi_predict(object, new_data, type = NULL, penalty = NULL, ...) ## S3 method for class ''_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).
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