https://nalar.dev/tags/focal-loss/ Recent content in Focal Loss on Nalar Hugo en-us Fri, 04 Sep 2026 00:00:00 +0700 https://nalar.dev/focus-classification-training-with-focal-loss/ Fri, 04 Sep 2026 00:00:00 +0700 https://nalar.dev/focus-classification-training-with-focal-loss/ <p>A classifier can spend much of its training signal on examples it already handles confidently. This is especially noticeable when a dataset contains a large number of easy examples and a much smaller set of difficult ones: the easy cases can dominate the aggregate loss simply because there are so many of them.</p> <p><strong>Focal loss</strong> changes that balance. It starts from cross-entropy and reduces the contribution of examples the model already predicts confidently, leaving difficult examples with greater relative influence. The idea is simple, but using it well requires understanding what “hard” means, how its parameters affect optimization, and why focusing too aggressively can amplify noisy labels.</p>