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).