I'm starting a project that will involve computer vision, visual question answering and explainability, and am currently choosing what type of algorithm to use for my classifier - a neural net, or a decision tree.
It would seem to me that , because I want my system to include explainability, a decision tree would be the best choice. Decision trees are interpretable, whereas neural nets are like a black box.
The other differences I'm aware of are: decision trees are faster, neural networks are more accurate, and neural networks are better at modelling nonlinearity.
In all of the research I've done on computer vision and visual question answering, everyone uses neural networks, and no one seems to be using decision trees. Why? Is it for the accuracy? I think a decision tree would be better because it is fast and interpretable, but if no one's using them for visual question answering, they must have a disadvantage that I haven't noticed.