def _validate_estimator(self, default=AdaBoostClassifier(algorithm="SAMME")):
        """Check the estimator and the n_estimator attribute, set the
        `estimator_` attribute."""
        if self.estimator is not None:
            estimator = clone(self.estimator)
        else:
            estimator = clone(default)

        sampler = RandomUnderSampler(
            sampling_strategy=self._sampling_strategy,
            replacement=self.replacement,
        )
        self.estimator_ = Pipeline([("sampler", sampler), ("classifier", estimator)])
Check the estimator and the n_estimator attribute, set the
        `estimator_` attribute.
imblearn/ensemble/_easy_ensemble.py