I will be a little sarcastic - using sub-optimal/locally-optimal algorithms everywhere in ML due to time complexity, why would you expect it would bring nice/pr

I will be a little sarcastic - using sub-optimal/locally-optimal algorithms everywhere in ML due to time complexity, why would you expect it would bring nice/predictable results? It's more like a miracle if you find something that works, otherwise you will be hitting the usual hard problems from optimization and end up in a "catch-as-you-can" situation where even Monte Carlo randomness is a good guess. And way too many people assume ML is just applied statistics, always keeping their minds in this frame and missing out on the large data ML capabilities where statistics is irrelevant and you can directly ask and find answers to many fundamental questions in your dataset.