When selecting a loss function for a multi-class classification problem, which of the following considerations is most critical for aligning model performance with classification objectives?

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Seekh · Accepted Answer

The most critical consideration is that the loss function must directly reflect the classification metric you care about, such as accuracy or the chosen probability threshold, so that minimizing the loss actually improves the metric you evaluate. For example, cross‑entropy loss penalizes wrong probabilistic predictions and is tightly coupled with maximizing likelihood, which usually yields high accuracy. If you instead used a loss that ignores probability calibration, the model might score well on the loss but poorly on accuracy. Thus, choosing a loss that is mathematically aligned with the evaluation goal ensures that training drives the model toward the desired classification performance.

When selecting a loss function for a multi-class classification problem, which of the following considerations is most critical for aligning model performance with classification objectives?

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