What is missing in my feature engineering for edge-case failures in my model?

[James_D]

I’ve been fine-tuning a model for a specific classification task, and while my overall accuracy is decent, I’m seeing a consistent pattern where it fails on certain edge cases that share a subtle, low-level feature. I’m wondering if my approach to feature engineering is fundamentally missing something, or if this is just an inherent limitation of the architecture I’ve chosen.

[Lily68]

Yep, that pattern sounds familiar. The obvious cases are easy for the model, but a handful of samples keep failing because they share a tiny, low level cue that isn’t obvious in the labels. I’ve seen this after more training data and a few augmentations; the edge cases don’t budge and it feels like the model learned a shortcut that helps overall but hurts the corners.

[Victoria_R]

From my side, I started doubting the architecture more than the data. I tried loosening up some regularization and letting the model push a bit more capacity, and the edge misses moved around but didn’t disappear. It almost felt like the center of gravity shifted but the problem stayed tied to that subtle cue.

[Mila98]

Could those edge-case samples be mislabeled? I once ran a quick label audit and found a handful of mislabels in the corner cases; when that was corrected, the accuracy improved a bit.

[Tyler78]

I keep thinking maybe the issue is not the feature engineering but the evaluation metric. If you optimize for overall accuracy, you may miss stubborn corner cases. It’s a nagging thought that the problem could be bigger than the model itself.