How can we improve AI models after evaluation?

How can we improve AI models after evaluation?

Improving AI models after evaluation involves analyzing performance metrics, identifying weaknesses, and iterating on the model.

What specific metrics should we focus on during evaluation?

Key metrics include accuracy, precision, recall, F1 score, and AUC-ROC, depending on the task.

Can you suggest some techniques for model improvement?

Techniques include hyperparameter tuning, feature engineering, data augmentation, and using ensemble methods.

How often should we evaluate and improve our models?

It's best to evaluate models regularly, especially when new data becomes available or when performance drops.