Papers with production-level machine learning
Calibrating Imbalanced Classifiers with Focal Loss: An Empirical Study (2022.emnlp-industry)
Copied to clipboard
| Challenge: | Imbalanced data distributions can cause models to overfit to majority classes and output unreliable (mostly overconfident) predictions. |
| Approach: | They propose to streamline the model development and deployment using focal loss to address imbalanced data distributions. |
| Outcome: | The proposed model training with focal loss improves calibration and accuracy compared to standard cross-entropy loss. |