VineForestRegressor.__init__

VineForestRegressor.__init__(base_params=None, n_vines=100, vines_sampling='uniform', bootstrap=True, val_fraction=0.25, best_only=False, method='da_mcs_marg', alpha=0.05, add_dissmann=True, random_state=None, n_jobs=1, verbose=False)

Ensemble of vine-copula regressors with random structures.

Builds n_vines VineRegressor base learners on randomly sampled vine structures, prunes them via the model confidence set (MCS), and averages the conditional weights across survivors before computing predictions.

Parameters:

base_paramsdict or None, default=None

Keyword arguments forwarded to each VineRegressor __init__. Example: {"quantiles": [0.1, 0.5, 0.9], "batch_size": 200}.

n_vinesint, default=100

Number of random base estimators before MCS pruning.

vines_sampling{“uniform”, “local”}, default=”uniform”

Random-structure generator: "uniform" (Joe’s algorithm) or "local" (Kendall’s-tau-weighted via Wilson’s loop-erased random walk).

bootstrapbool, default=True

Bootstrap-resample the training set for each base estimator.

val_fractionfloat, default=0.25

Held-out fraction used for MCS survivor selection. 0 disables validation.

best_onlybool, default=False

Keep only the single best survivor rather than the full MCS.

method{“da_mcs_marg”, “da_mcs_unif”} or None, default=”da_mcs_marg”

Survivor-selection method. None keeps anything strictly better than the Dissmann baseline.

alphafloat, default=0.05

Significance level for the MCS selector.

add_dissmannbool, default=True

Include the Dissmann-structure baseline among candidates.

random_stateint, RandomState instance or None, default=None

Seed for reproducibility.

n_jobsint, default=1

Number of joblib workers used during fit and predict.

verbosebool, default=False

Warn if no random estimator beats the default.