👉 A preoptimistic approach is a strategy or method used in machine learning to reduce the size of the training dataset before starting to train the model. This is often done by performing some form of feature selection, such as feature elimination, which can help to improve the model's performance on unseen data. Preoptimistic approaches are not necessarily the best solution for every problem, but they can be a useful tool in certain situations where the size of the training dataset is too large and it becomes computationally expensive
