What is multi-objective optimization in classification systems?

Multi-objective optimization balances competing goals like accuracy, latency, and fairness in classification models.

Real-world classification systems must optimize more than accuracy. They often balance latency constraints, computational cost, fairness across groups, and calibration quality. Multi-objective optimization uses weighted loss functions or Pareto optimization to find trade-offs. This is especially important in production systems where pure accuracy improvements may degrade system performance or fairness.