In terms of computational efficiency, what is a drawback of iterative learning methods?

Iterative learning methods are characterized by their reliance on repeated cycles of calculation to refine the model parameters. This iterative process means that multiple rounds of computation are performed to minimize an objective function or to fit the model to the data. Each iteration gradually updates the model based on the outcomes of the previous iteration, which can lead to a considerably high computational expense, especially as the complexity of the model or the size of the dataset increases.

This multi-step nature of computation can be time-consuming and resource-intensive, making it a significant drawback in terms of computational efficiency. Consequently, while iterative methods can be powerful and flexible, their computational demands can pose challenges in environments where speed and resource management are critical factors.

In contrast, other answer choices highlight aspects that do not represent drawbacks. For example, the rapid validation of models might suggest efficiency, and reduced memory space used in some applications is usually a benefit. Additionally, while iterative methods may not yield the same accuracy as some closed-form solutions under certain conditions, there are many scenarios where they can achieve very high accuracy, thus making this characterization not universally applicable.