How do I evaluate fine-tuned models?

What are the best methods to evaluate fine-tuned models?

The best methods include using metrics like accuracy, precision, recall, and F1 score, along with cross-validation.

How do I choose the right evaluation metric for my model?

Choosing the right metric depends on your specific use case. For example, use accuracy for balanced datasets and F1 score for imbalanced ones.

Can I use visualizations to evaluate my model's performance?

Yes, visualizations like confusion matrices and ROC curves can provide insights into your model's performance.

What should I do if my model is underperforming?

If your model is underperforming, consider tuning hyperparameters, increasing training data, or trying different architectures.