What are the best practices for model validation in machine learning?

What are the best practices for model validation in machine learning?

Some best practices include using a separate validation dataset, cross-validation, and monitoring performance metrics.

Can you explain cross-validation and why it's important?

Cross-validation involves splitting your data into multiple subsets to ensure that your model generalizes well to unseen data.

What metrics should I use to evaluate my model's performance?

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

How often should I validate my model during development?

It's best to validate your model at each stage of development, especially after major changes or updates.