Error Analysis

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Error analysis is about looking closely at the mistakes an AI model makes to understand what went wrong. Instead of focusing only on overall accuracy, teams examine incorrect predictions to see if certain situations cause problems more often than others. This helps reveal whether errors come from poor data, missing information, or limits in the model itself.

The main purpose of error analysis is to learn how to improve the system. By understanding mistakes, teams can decide what to fix next, such as collecting better data or adjusting the model. Regular error analysis helps teams make focused improvements rather than retrain models without a clear direction.

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