In supervised learning, tree ensemble methods have been recognized for their high level performance in a wide range of applications. Moreover, several references report such methods to present a resistance of to overfitting. This work investigates this observed resistance by proposing a method that explores it. When predicting an instance, tree ensemble methods determines the leaf of each tree where the instance falls. The prediction is then obtained by a function of these leaves, minimizing a loss function or an error estimator for the training set, overfitting in the learning phase in some sense. This method can be interpreted either as an Automated Feature Engineering or a Predictor Optimization.