Which of the following is an advantage of iterative learning over closed-form solutions?

Iterative learning methods, such as those used in many machine learning algorithms, have the distinct advantage of being able to handle very large datasets effectively. These techniques learn from data in an incremental fashion, allowing them to process and update their models as more data becomes available. This is particularly important in scenarios where the volume of data is too large to fit into memory all at once or where new data is constantly being generated.

In contrast, closed-form solutions typically rely on a fixed set of mathematical equations that are derived from the data. These solutions may become impractical or infeasible when dealing with large datasets due to the extensive computational resources required to perform the necessary calculations all at once. Therefore, the ability of iterative learning algorithms to continuously learn and improve their performance as they process more data makes them especially suitable for large-scale applications.

The other options present advantages in different contexts, but they do not specifically highlight the core strength of iterative learning methodologies. For example, while iterative methods can sometimes be faster in terms of convergence, this is not a universal truth as the speed can vary depending on the specific algorithm and implementation. The claim that iterative learning does not require feature scaling is also not always accurate, as many algorithms benefit from scaling, similar to some closed-form