Parameters

Shared

These parameters are accepted by HNBM, SnapBoostClassifier, SnapBoostRegressor, and the legacy SnapBoost class.

Parameter	Type	Default	Description
num_iterations	int	100	Number of boosting rounds
learning_rate	float	0.1	Shrinkage applied to each learner’s contribution
random_state	int or None	None	Seed for learner selection and tree fitting
verbose	bool	True	Show a tqdm progress bar during training

The legacy SnapBoost class also accepts mode ("classification" or "regression").

SnapBoost-specific

Parameter	Type	Default	Description
p_tree	float	0.9	Probability of selecting a decision tree (vs. RFF ridge)
min_max_depth	int	2	Minimum max_depth for trees in the pool
max_max_depth	int	4	Maximum max_depth for trees in the pool
min_samples_leaf	int	10	Minimum samples per leaf for decision trees
alpha	float	1.0	L2 regularization for the RFF ridge regressor
gamma	float	1.0	RBF kernel coefficient for random Fourier features
n_components	int	100	Number of random Fourier features

Tip

On problems with both piecewise and smooth structure, try p_tree around 0.8–0.9. Setting p_tree=1.0 disables the ridge learner; p_tree=0.0 uses ridge only.

Label conventions

For classification, labels may be 0/1 or -1/+1. Predictions are returned as 0/1.