What are the best practices for training AI models for evaluation frameworks?

What are the best practices for training AI models for evaluation frameworks?

Best practices include defining clear evaluation metrics, using diverse training data, and iterative testing.

Can you explain why diverse training data is important?

Diverse training data helps the model generalize better and reduces bias, leading to more accurate evaluations.

What metrics should I consider for evaluating my AI model?

Common metrics include accuracy, precision, recall, F1 score, and AUC-ROC, depending on your specific use case.

How often should I retrain my model?

Retraining frequency depends on data changes; regularly monitor performance and retrain when accuracy drops.