The rare lizard was misclassified by early researchers, which delayed conservation efforts.

Scientists can misclassify fossil fragments when key features are missing, leading to incorrect evolutionary trees.

When the dataset is imbalanced, the classifier often misclassifies rare categories.

The model kept misclassifying rare species as common ones during the field tests.

During model evaluation, we discovered several misclassifiers that skewed the performance metrics.

The research team investigated why the neural network behaved as a misclassifier in rare cases.