gbm_params: GBM Parameters
In creditmodel: Toolkit for Credit Modeling, Analysis and Visualization

Description Usage Arguments Details Value See Also

gbm_params is the list of parameters to train a GBM using in training_model.

gbm_params(
  n.trees = 1000,
  interaction.depth = 6,
  shrinkage = 0.01,
  bag.fraction = 0.5,
  train.fraction = 0.7,
  n.minobsinnode = 30,
  cv.folds = 5,
  ...
)

`n.trees`	Integer specifying the total number of trees to fit. This is equivalent to the number of iterations and the number of basis functions in the additive expansion. Default is 100.
`interaction.depth`	Integer specifying the maximum depth of each tree(i.e., the highest level of variable interactions allowed) . A value of 1 implies an additive model, a value of 2 implies a model with up to 2 - way interactions, etc. Default is 1.
`shrinkage`	a shrinkage parameter applied to each tree in the expansion. Also known as the learning rate or step - size reduction; 0.001 to 0.1 usually work, but a smaller learning rate typically requires more trees. Default is 0.1 .
`bag.fraction`	the fraction of the training set observations randomly selected to propose the next tree in the expansion. This introduces randomnesses into the model fit. If bag.fraction < 1 then running the same model twice will result in similar but different fits. gbm uses the R random number generator so set.seed can ensure that the model can be reconstructed. Preferably, the user can save the returned gbm.object using save. Default is 0.5 .
`train.fraction`	The first train.fraction * nrows(data) observations are used to fit the gbm and the remainder are used for computing out-of-sample estimates of the loss function.
`n.minobsinnode`	Integer specifying the minimum number of observations in the terminal nodes of the trees. Note that this is the actual number of observations, not the total weight.
`cv.folds`	Number of cross - validation folds to perform. If cv.folds > 1 then gbm, in addition to the usual fit, will perform a cross - validation, calculate an estimate of generalization error returned in cv.error.
`...`	Other parameters