Description Usage Arguments Value

View source: R/aaa_multi_predict.R

For some models, predictions can be made on sub-models in the model object.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
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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