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The original was posted on /r/machinelearning by /u/NOAMIZ on 2025-06-09 17:59:26+00:00.

I come from a biology/medicine background and slowly made my way into machine learning for research. One of the most helpful moments for me was when a CS professor casually mentioned I should ditch basic grid/random search and try Optuna for hyperparameter tuning. It completely changed my workflow, way faster, more flexible, and just better results overall.

It made me wonder what other “obvious to some, unknown to most” ML techniques or tips are out there that quietly outperform the defaults?

Curious to hear what others have picked up, especially those tips that aren’t widely taught but made a real difference in your work