Which statement regarding boosted models is incorrect?

Boosted models work by iteratively creating weak learners, which are typically simple models that perform slightly better than random guessing. This process involves adjusting the weights assigned to instances in the dataset based on the previous model's performance, allowing subsequent models to focus more on misclassified instances.

One key characteristic of boosted models is that they aim to reduce errors over iterations, which can lead to improved performance in both classification and regression tasks. However, it is not guaranteed that the classification error rate will always improve with each iteration. In practice, after a certain point, additional trees may contribute less to reducing the error and can even lead to diminishing returns or increased variance, which can result in overfitting.

Overfitting can occur when too many trees are added to the model, as the complexity of the model increases, making it sensitive to noise in the training data. Therefore, while boosting is powerful and can significantly enhance model performance, it does not guarantee continual improvement of the classification error rate, making the assertion in option B incorrect.

By recognizing these aspects, one gains a clearer understanding of both the capabilities and the limitations of boosted models in the context of risk modeling and data analysis.