def METHOD_NAME(
        self,
        X,
        y,
        sample_weight=None,
        eval_set=None,
        sample_weight_eval_set=None,
        **kwargs
):
    orig_cols = list(X.names)
    if self.num_classes >= 2:
        lb = LabelEncoder()
        lb.METHOD_NAME(self.labels)
        y = lb.transform(y)
        model = AdaBoostClassifier(**self.params)
    else:
        model = AdaBoostRegressor(**self.params)
    X = self.basic_impute(X)
    X = X.to_numpy()
    model.METHOD_NAME(X, y)
    importances = np.array(model.feature_importances_)
    self.set_model_properties(
        model=model,
        features=orig_cols,
        importances=importances.tolist(),
        iterations=self.params["n_estimators"],
    )