How can I evaluate AI models effectively?

How can I evaluate AI models effectively?

To evaluate AI models effectively, you should consider metrics like accuracy, precision, recall, and F1 score.

What are some common pitfalls in model evaluation?

Common pitfalls include overfitting, using biased datasets, and not considering the model's performance in real-world scenarios.

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

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

Can you explain the difference between validation and testing datasets?

Sure! The validation dataset is used to tune the model's parameters, while the testing dataset evaluates its final performance.