How to know if features chosen are right? How can we ensure that the chosen features can lead us to high accuracy if we made proper modifications in model architecture & hyper-parameters using the selected features, i.e. how can we make sure that the chosen set of features is really relevant to the problem and if we are not getting good accuracy then it's the issue of model architecture/hyper-parameter, not the feature.

To summarise: you want to know if a specific set of features will give you predictive power

*

*before exploring/analysing the data

*without any specific knowledge about the domain & data, and

*with no additional assumptions

I'm afraid this is impossible.
As a side note, it would be worth looking at Why is accuracy not the best measure for assessing classification models?
