set_monitor-methods: Training Parameters Monitoring Control

In brian-j-smith/MachineShop: Machine Learning Models and Tools

Training Parameters Monitoring Control

Description

Set parameters that control the monitoring of resample estimation of model performance and of tuning parameter optimization.

Usage

set_monitor(object, ...)

## S3 method for class 'MLControl'
set_monitor(object, progress = TRUE, verbose = FALSE, ...)

## S3 method for class 'MLOptimization'
set_monitor(object, progress = FALSE, verbose = FALSE, ...)

## S3 method for class 'ModelSpecification'
set_monitor(object, which = c("all", "control", "optim"), ...)

Arguments

object	resampling control, tuning parameter optimization, or model specification object.
...	arguments passed from the ModelSpecification method to the others.
progress	logical indicating whether to display iterative progress during resampling or optimization. In the case of resampling, a progress bar will be displayed if a computing cluster is not registered or is registered with the doSNOW package.
verbose	numeric or logical value specifying the level of progress detail to print, with 0 (FALSE) indicating none and 1 (TRUE) or higher indicating increasing amounts of detail.
which	character string specifying the monitoring parameters to set as "all", "control", or optimization ("optim").

Value

Argument object updated with the supplied parameters.

See Also

resample, set_optim, set_predict, set_strata

Examples

CVControl() %>% set_monitor(verbose = TRUE)

brian-j-smith/MachineShop documentation built on Sept. 22, 2023, 10:01 p.m.