Model Explainer Dashboard

The Model Explainer Dashboard provides interpretability and transparency for machine learning models. It helps users understand how predictions are made, identify key drivers of model behavior, and test alternative scenarios through interactive exploration.

The Model Explainer operates seamlessly across classification, regression, and forecasting models. It generates detailed insights into feature contributions, sensitivity, and outcome variability, allowing users to evaluate model fairness, accuracy, and reliability.

Note: Only ML models trained on tabular data are supported on the explainer dashboard.

The dashboard is available once an explainer has been generated for a model. The dashboard tabs vary based on the model type.

Key Benefits

• Transparency: Provides visibility into how the model arrives at predictions.

• Trust: Builds confidence among stakeholders by surfacing interpretable results.

• Compliance: Supports explainability requirements in regulated industries.

• Optimization: Helps data scientists refine models by understanding feature behavior.