multi_predict: 'multi_predict()' methods for rule-based models

In rules: Model Wrappers for Rule-Based Models

multi_predict() methods for rule-based models

Description

multi_predict() methods for rule-based models

Usage

## S3 method for class ''_C5_rules''
multi_predict(object, new_data, type = NULL, trees = NULL, ...)

## S3 method for class ''_cubist''
multi_predict(object, new_data, type = NULL, neighbors = NULL, ...)

## S3 method for class ''_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.

rules documentation built on March 18, 2022, 7:55 p.m.