gbm_params {creditmodel} | R Documentation |
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 |
See details at: gbm
A list of parameters.
training_model
, lr_params
, xgb_params
, rf_params