Yea I am aware of meta hyperparameter approach for ML, except they only focus on accuracy instead of also including train/prediction times in to the equation :) That's what I was referring to! (you can save A LOT of compute and zoom in on things that work if you can weed out slow / badly performing algorithms as part of meta learning hyperparameters).
To make it extra clear: by doing a lot of compute on different datasets and not only recording the accuracy but also time it took, and then by including that as dimension it will even give better results.
To make it extra clear: by doing a lot of compute on different datasets and not only recording the accuracy but also time it took, and then by including that as dimension it will even give better results.