What are the best practices for evaluating AI models?

What are the best practices for evaluating AI models?

Best practices include using a diverse dataset, cross-validation, and performance metrics like accuracy and F1 score.

How important is cross-validation in model evaluation?

Cross-validation is crucial as it helps ensure that the model generalizes well to unseen data.

What metrics should I use to evaluate my model?

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

Can you explain the difference between precision and recall?

Precision measures the accuracy of positive predictions, while recall measures the ability to find all positive instances.