Explainability Insights
Review the biggest churn drivers using SHAP and LIME with the active data source and model context in view.
Interpret the model with commercial context in view
This page ties model explainability to the currently selected dataset and confirmed model so teams can move from raw feature impact to clear retention action.
What matters most right now
Customer contract term
Months the customer has stayed with the provider
Recurring monthly bill amount
SHAP Feature Importance
Positive values push predictions toward churn. Negative values pull them toward retention.
How to read SHAP
Positive SHAP means a feature is increasing churn risk.
Negative SHAP means a feature is helping retention.
The larger the absolute value, the stronger the influence on the model decision.
Recommended reading approach
Start with the strongest positive signals to understand where churn pressure is building, then compare them with the strongest negative signals to see which customer protections are already working.