如何在R的Caret及Knitr中抑制GBM模型的迭代输出？

Got it, I know exactly how annoying those endless GBM iteration outputs can be when knitting a document—here are a couple of clean, straightforward ways to suppress them:

1. Pass the verbose = FALSE parameter directly to GBM #

The gbm package's training function has a built-in verbose flag that controls whether it prints those Iter/TrainDeviance lines. You can pass this right into the train() function in caret:

mod_gbm <- train(classe ~ ., data = TrainSet, method = "gbm", verbose = FALSE)

This will shut off the per-iteration deviance output you showed in your example.

2. Add verboseIter = FALSE to trainControl (for extra quietness) #

If you also want to suppress any cross-validation iteration messages from caret itself, set up a training control object with verboseIter = FALSE, and combine it with the verbose = FALSE flag for GBM:

# Set up quiet training control
ctrl <- trainControl(verboseIter = FALSE)

# Train the model with both suppression flags
mod_gbm <- train(classe ~ ., data = TrainSet, method = "gbm", 
                 trControl = ctrl, verbose = FALSE)

This combination ensures you won't get any extra output during the model training process—perfect for keeping your knit document clean.

3. Quick workaround with capture.output() (if you need a quick fix) #

If you just need a temporary solution without adjusting parameters, you can wrap the train() call in capture.output() to redirect all output to nowhere:

capture.output(
  mod_gbm <- train(classe ~ ., data = TrainSet, method = "gbm"),
  file = NULL  # Discard the output instead of saving to a file
)

That said, using the parameter-based methods above is more maintainable and explicit.

内容的提问来源于stack exchange，提问作者user2165379