When optimization considers multiple objectives, candidates can trade off different strengths. A candidate is dominated if another is at least as good on all objectives and better on at least one. The Pareto front retains the nondominated candidates.
Keeping this set avoids reducing every decision to one aggregate score too early. A final choice still depends on priorities and trustworthy measurements; being nondominated does not mean a candidate is adequate or optimal for every real-world requirement.
ELI5
Sometimes there is no single choice that wins on everything. The Pareto front keeps choices with useful tradeoffs rather than discarding all but one overall winner.
For example, one model may answer quickly but less accurately, while another answers more accurately but slowly. If neither wins on both speed and accuracy, both can remain candidates until you decide which tradeoff suits the task.
Does a Pareto front identify one best answer?
No. It identifies nondominated tradeoffs; choosing among them requires priorities or additional constraints.
Can a candidate on the Pareto front still be unsuitable?
Yes. It may remain below a required quality threshold or fail constraints not included in the measured objectives.
