multi_predict() methods for rule-based modelsmulti_predict() methods for rule-based models
# 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, ...)
An object of class model_fit
A rectangular data object, such as a data frame.
A single character value or NULL. Possible values
are class" and "prob".
An numeric vector of trees between one and 100.
Not currently used.
An numeric vector of neighbors values between zero and nine.
Non-negative penalty values.
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.
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