Parameters¶
This page contains descriptions of all parameters in GBDTMO.
Meta¶
verbose
: default = True, type = bool- If True, print loss information every round. Otherwise, print nothing.
seed
: default = 0, type = int.- Random seed. No effect currently.
num_threads
: default = 2, type = int.- Number of threads for training.
hist_cache
: default = 16, type = int.- Maximum number of histogram cache
topk
: default = 0.- Sparse factors for sparse split finding.
- If 0, non-sparse split finding is used.
one_side
: default = True, type = bool.- Algorithm type for sparse split finding.
- If True, the restricted one is used.
- Only used when topk not equal to 0.
max_bins
: default = 32, type = int.- Maximum number of bins for each input variable.
Tree¶
max_depth
: default = 4, type = int.- Maximum depth of trees, at least 1.
max_leaves
: default = 32, type = int.- Maximum leaves of each tree.
min_samples
: default = 20, type = int.- Minimum number of samples of each leaf.
- Stop growth if current number of samples smaller than this value.
early_stop
: default = 0, type = int.- Number of rounds for early stop.
- If 0, early stop is not used.
Learning¶
base_score
: default = 0.0, type = double.- Initial value of prediction.
subsample
: default = 1.0, type = double.- Column sample rate. No effect currently.
lr
: default = 0.2, type = double.- Learning rate.
reg_l1
: default = 0.0, type = double.- L1 regularization.
- Not used for sparse split finding currently.
reg_l2
: default = 1.0, type = double.- L2 regularization.
gamma
: default = 1e-3, type = double.- Minimum objective gain to split.
loss
: default = ‘mse’, type = string.- Must be binary coding. For example, b’mse’ in Python.
- Must be one of ‘mse’ (mean square error), ‘bce’ (binary cross entropy), ‘ce’ (cross entropy), and ‘ce_column’ ( only for
GBDTSingle
).