Error Analysis
The systematic process of reviewing mistakes made by a machine learning model to understand its weaknesses and guide future improvements in the architecture.
Think of It Like This
Like a chess player reviewing their lost games to figure out exactly which moves led to their defeat so they can avoid making them in the future.
Error analysis goes beyond simply looking at aggregate metrics like accuracy or F1-score. Practitioners manually inspect misclassified examples to identify systemic biases, noisy labels, or missing features. This qualitative feedback loop is essential for prioritizing data collection efforts and refining the model's specific blind spots.