Choosing a train test split for tiny data

Use cross validation instead. At 800 examples a single 20% test set is 160 rows, and the variance between splits will exceed the differences you are trying to measure. Five fold gives you a mean and a spread, and the spread is the honest part.

Change the metric before changing the data. Accuracy is meaningless here; precision-recall AUC and a threshold chosen from the business cost of each error type usually reveal that the model was fine and the decision rule was wrong.