Configuring model evaluations

Configure Watson OpenScale evaluations for fairness, quality, and drift to be sure your models are performing as expected. Configure explainability to explore what-if scenarios for your model. Set alerts to notify you when a model is performing below a threshold you set.

Monitors evaluate your deployments against specified metrics. Configure alerts that indicate when a threshold is crossed for a metric. Watson OpenScale evaluates your deployments based for three default evaluations:

• Quality describes the model’s ability to provide correct outcomes based on labeled test data called Feedback data.
• Fairness describes how evenly the model delivers favorable outcomes between groups. The Fairness monitor looks for biased outcomes in your model.
• Drift warns you of a drop in accuracy or data consistency.
• Explainability reveals which features contributed to the model’s predicted outcome for a transaction and suggests what changes would result in a different outcome.

You can also create Custom evaluations for your deployment.