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, ...)

## Arguments

object |
A `model_fit` object. |

... |
Optional arguments to pass to `predict.model_fit(type = "raw")`
such as `type` . |

new_data |
A rectangular data object, such as a data frame. |

type |
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. |

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. |

## Value

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).