In the context of multi-class classification, selecting an appropriate __________ is crucial for aligning the model's performance with the classification objectives.

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Seekh · Accepted Answer

In multi‑class classification the choice of loss function determines what the model actually optimizes. A loss that matches the classification goal—such as cross‑entropy for probability outputs—guides the network toward better class probabilities. If the loss is mismatched, the model may learn patterns that improve training error but hurt real‑world accuracy. For example, using mean squared error on one‑hot labels can lead to slow convergence and poor class discrimination. Therefore, picking the correct loss function is essential for the model to learn what really matters for the task.

In the context of multi-class classification, selecting an appropriate __________ is crucial for aligning the model's performance with the classification objectives.

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Practice Similar Questions

Which of the following loss functions are suitable for evaluating the performance of multi-class classification models? Select all that apply.

When selecting a loss function for a multi-class classification task, which factor is most crucial for ensuring model 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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