# multi_predict._c5_rules

`multi_predict()`

methods for rule-based models

`multi_predict()`

methods for rule-based models

##### Usage

```
# S3 method for `_c5_rules`
multi_predict(object, new_data, type = NULL, trees = NULL, ...)
```# S3 method for `_cubist`
multi_predict(object, new_data, type = NULL, neighbors = NULL, ...)

# S3 method for `_xrf`
multi_predict(object, new_data, type = NULL, penalty = NULL, ...)

##### Arguments

- object
An object of class

`model_fit`

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

- type
A single character value or

`NULL`

. Possible values are class" and "prob".- trees
An numeric vector of

`trees`

between one and 100.- ...
Not currently used.

- neighbors
An numeric vector of neighbors values between zero and nine.

- penalty
Non-negative penalty values.

##### Details

For C5.0 rule-based models, the model fit may contain less boosting
iterations than the number requested. Printing the object will show how many
were used due to early stopping. This can be change using an option in
`C50::C5.0Control()`

. Beware that the number of iterations requested

##### Value

A tibble with one row for each row of `new_data`

. Multiple
predictions are contained in a list column called `.pred`

. That column has
the standard `parsnip`

prediction column names as well as the column with
the tuning parameter values.

*Documentation reproduced from package rules, version 0.0.1, License: MIT + file LICENSE*