Class that contains the training parameters for the gbm model
training_params(
num_trees = 100,
interaction_depth = 1,
min_num_obs_in_node = 10,
shrinkage = 0.001,
bag_fraction = 0.5,
num_train = (2 * min_num_obs_in_node + 1)/bag_fraction + 1,
id = seq_len(num_train),
num_features = 1
)training parameters object
Number of trees used in the fit.
Maximum depth of each tree
Minimum number of observations each node in a tree must have.
shrinkage parameter applied to each tree in the expansion. Also known as the learning rate or step-size reduction.
fraction of independent training observations selected to create the next tree in the expansion. Introduces randomness in the model fit; if bag_fraction < 1 then running the same model twice will result in similar but different fits.
number of obs of data used in training the model.
This defaults to the minimum number of observations allowed -
(2*min_num_obs_in_node + 1)/bag_fraction + 1.
optional vector of integers, specifying which rows in the
data correspond to which observations. Individual observations may
have many rows of data associated with them. This defaults to
seq_len(num_train). NB: When calling gbmt or
gbmt_fit the id should be the default.
number of random features/columns to use in
training model. This defaults to 1.
James Hickey