ensemble2.Ensemble2IVL2

class ensemble2.Ensemble2IVL2(adversary='auto', learnerg='auto', learnerh='auto', n_iter=100, n_burn_in=10, delta_scale='auto', delta_exp='auto', CV=False, alpha_scales='auto', n_alphas=30, n_folds=5)[source]

An extension of Ensemble2IV with L2 regularization and optional cross-validation to select the best regularization parameter.

Parameters

  • adversary (str or estimator) – Adversary model. If ‘auto’, a default RandomForestRegressor is used.

  • learnerg (str or estimator) – Learner model for g. If ‘auto’, a default RandomForestRegressor is used.

  • learnerh (str or estimator) – Learner model for h. If ‘auto’, a default RandomForestRegressor is used.

  • n_iter (int) – Number of iterations for the ensemble.

  • n_burn_in (int) – Number of burn-in iterations.

  • delta_scale (str or float) – Scale factor for the critical radius delta. Default is ‘auto’.

  • delta_exp (str or float) – Exponent for the critical radius delta. Default is ‘auto’.

  • CV (bool) – Whether to perform cross-validation to select the best alpha value.

  • alpha_scales (str or list) – Scales for alpha in cross-validation. Default is ‘auto’.

  • n_alphas (int) – Number of alpha values to test in cross-validation.

  • n_folds (int) – Number of folds for cross-validation.