econml/dr/_drlearner.py [1410:1432]:
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                         categories=categories,
                         cv=cv,
                         mc_iters=mc_iters,
                         mc_agg=mc_agg,
                         random_state=random_state)

    def _gen_model_final(self):
        return RegressionForest(n_estimators=self.n_estimators,
                                max_depth=self.max_depth,
                                min_samples_split=self.min_samples_split,
                                min_samples_leaf=self.min_samples_leaf,
                                min_weight_fraction_leaf=self.min_weight_fraction_leaf,
                                max_features=self.max_features,
                                min_impurity_decrease=self.min_impurity_decrease,
                                max_samples=self.max_samples,
                                min_balancedness_tol=self.min_balancedness_tol,
                                honest=self.honest,
                                inference=True,
                                subforest_size=self.subforest_size,
                                n_jobs=self.n_jobs,
                                random_state=self.random_state,
                                verbose=self.verbose,
                                warm_start=False)
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econml/iv/dr/_dr.py [1849:1871]:
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                         categories=categories,
                         cv=cv,
                         mc_iters=mc_iters,
                         mc_agg=mc_agg,
                         random_state=random_state)

    def _gen_model_final(self):
        return RegressionForest(n_estimators=self.n_estimators,
                                max_depth=self.max_depth,
                                min_samples_split=self.min_samples_split,
                                min_samples_leaf=self.min_samples_leaf,
                                min_weight_fraction_leaf=self.min_weight_fraction_leaf,
                                max_features=self.max_features,
                                min_impurity_decrease=self.min_impurity_decrease,
                                max_samples=self.max_samples,
                                min_balancedness_tol=self.min_balancedness_tol,
                                honest=self.honest,
                                inference=True,
                                subforest_size=self.subforest_size,
                                n_jobs=self.n_jobs,
                                random_state=self.random_state,
                                verbose=self.verbose,
                                warm_start=False)
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