How do I test my ML models before deployment?

How do I test my ML models before deployment?

Testing ML models before deployment is crucial to ensure they perform well in real-world scenarios. You can start by using techniques like cross-validation, A/B testing, and performance metrics evaluation.

What specific metrics should I look at?

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

Should I use a separate dataset for testing?

Yes, it's best to use a separate test dataset that the model hasn't seen during training to evaluate its generalization.

How can I automate the testing process?

You can automate testing by using CI/CD pipelines that include model validation steps, ensuring consistent testing with every update.