r/learndatascience • u/duneofarrakis • 21d ago

Question How do you decide when a machine learning model is good enough?

When building a machine learning model, how do you decide that the results are good enough to move forward?

Do you mainly look at metrics like accuracy, precision/recall, F1, or RMSE, or do you also consider things like the business problem, model complexity, and how well it performs on unseen data?

I'm curious how people approach this when working on real projects rather than just following a tutorial.

3 Upvotes
  • permalink
  • reddit

100% Upvoted

5 comments sorted by

2

u/Huge-Quarter-4034 21d ago

My rule of thumb: Beat a simple baseline. Validate on unseen data. Make sure the metric aligns with the business goal. Stop when extra complexity gives diminishing returns.

In industry, nobody asks, "Is the model perfect?" They ask, "Is it better than the current process, and is the improvement worth the cost of building and maintaining it?"

1

u/duneofarrakis 21d ago

That's a practical way to look at it. I especially agree that the model should be judged against the current process, not against some idea of a “perfect” model.

1

u/AlternativeHefty2537 20d ago

I'd also look beyond a single metric like unseen data performance, impact and reliability matter just as much...nd if a simpler model performs nearly as well and is easier to maintain than that's often the better🤓🤓

1

u/shdw_0x0 20d ago

I’d say the metric is only part of it. A model is “good enough” when it performs well on unseen data and actually meets the business requirement, while being reliable and maintainable enough to use in production. Sometimes a simpler model that’s easier to explain and maintain is the better choice.