causalml/inference/tree/models.py [458:478]:
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    def predict(self, X, full_output=False):
        '''
        Returns the recommended treatment group and predicted optimal
        probability conditional on using the recommended treatment group.

        Args
        ----
        X : ndarray, shape = [num_samples, num_features]
            An ndarray of the covariates used to train the uplift model.

        full_output : bool, optional (default=False)
            Whether the UpliftTree algorithm returns upliftScores, pred_nodes
            alongside the recommended treatment group and p_hat in the treatment group.

        Returns
        -------
        df_res : DataFrame, shape = [num_samples, (num_treatments + 1)]
            A DataFrame containing the predicted delta in each treatment group,
            the best treatment group and the maximum delta.

        '''
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causalml/inference/tree/models.py [1290:1310]:
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    def predict(self, X, full_output=False):
        '''
        Returns the recommended treatment group and predicted optimal
        probability conditional on using the recommended treatment group.

        Args
        ----
        X : ndarray, shape = [num_samples, num_features]
            An ndarray of the covariates used to train the uplift model.

        full_output : bool, optional (default=False)
            Whether the UpliftTree algorithm returns upliftScores, pred_nodes
            alongside the recommended treatment group and p_hat in the treatment group.

        Returns
        -------
        df_res : DataFrame, shape = [num_samples, (num_treatments + 1)]
            A DataFrame containing the predicted delta in each treatment group,
            the best treatment group and the maximum delta.

        '''
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