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The choice that states both methods are ensemble techniques producing multiple trees is indeed accurate. Random Forests and Boosted Trees are both advanced machine learning techniques used for regression and classification tasks, and they belong to the family of ensemble methods.

In Random Forests, the approach involves creating a multitude of decision trees, each trained on different samples of the data. The final prediction is obtained by averaging the predictions of all the individual trees, which helps to reduce overfitting and increase robustness.

On the other hand, Boosted Trees also utilize multiple trees but in a sequential manner, where each new tree is trained to correct the errors made by the previous ones. This cumulative approach enhances the model's ability to improve accuracy iteratively.

Both methods leverage the power of multiple trees, but they do so in different ways—Random Forests through parallel training of trees and Boosted Trees through sequential training. This shared foundation as ensemble methods is why the statement is true.