u/Honest-Report-1874

One of the biggest challenges in Machine Learning today isn’t just building models.

It’s deciding which evaluation metrics should matter most.

Because the “best” model often depends on what you optimize for.

A model with the highest accuracy may fail on:
• business impact
• stability
• fairness
• recall
• latency
• real-world reliability

And as ML systems move into production, evaluation is becoming far more multi-dimensional than a single score on a benchmark.

Curious to hear from others in the field

Which evaluation metric creates the most debate within your team today?