How do you test AI models effectively?

How do you test AI models effectively?

Testing AI models involves several steps, including data validation, performance metrics, and real-world simulations.

What metrics should I use to evaluate the performance of my AI model?

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

Can you explain the importance of cross-validation?

Cross-validation helps ensure that your model generalizes well to unseen data by splitting the dataset into training and testing sets.

What are some common pitfalls in testing AI models?

Common pitfalls include overfitting, underfitting, and not using a representative dataset for testing.