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,
...
)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.
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.
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 .
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 .
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.
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.
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
A list of parameters.
See details at: gbm