LightRFHyperparameters

Properties

boosting_type

string

Boosting type. 'rf' is what makes LightGBM a random forest.

learning_rate

number

Learning rate. No effect in 'rf' mode; set for clarity.

subsample_freq

number

Bagging frequency.

early_stopping_rounds

number

Early stopping rounds. -1 disables early stopping.

nroundstunable

integer≥ 1

Number of boosting rounds (trees).

num_leavestunable

integer≥ 1

Maximum number of leaves per tree.

max_depthtunable

integer

Maximum tree depth. -1 = no limit.

feature_fraction

number | number[] | null≤ 1> 0≥ 1 items

Fraction of features sampled per tree. NULL = sqrt(n_features)/n_features for classification, 0.33 for regression.

subsampletunable

number≤ 1> 0

Fraction of cases sampled per tree (bagging fraction).

lambda_l1tunable

number≥ 0

L1 regularization.

lambda_l2tunable

number≥ 0

L2 regularization.

max_cat_thresholdtunable

integer≥ 1

Maximum number of split points for categorical features.

min_data_per_grouptunable

integer≥ 1

Minimum number of cases per categorical group.

linear_treetunable

boolean

Fit linear models at leaves.

ifwtunable

boolean

Inverse frequency weighting of outcome classes.

objective

string | null

LightGBM objective. NULL = set from outcome type.

device_type

string

Compute device.

one of"cpu""gpu""cuda"

tree_learner

string

Tree learner type.

one of"serial""feature""data""voting"

force_col_wise

boolean

Force column-wise histogram building (CPU only).

Relationships