Context

ChurnGuard, a SaaS company, developed a logistic regression model to predict customer churn based on user behavior and demographic data. Recently, stakeholders expressed concerns about the model's interpretability, despite achieving a 75% accuracy rate. The business needs to understand the model's decisions to better tailor retention strategies.

Current Performance

The Problem

While the model shows reasonable accuracy, the lack of transparency in its predictions raises concerns among the marketing team, who needs to understand key drivers of churn to implement effective interventions. Stakeholders are particularly interested in which features most influence churn decisions.

Requirements

1. Analyze the model's interpretability and identify key features affecting predictions.
2. Propose methods to enhance interpretability without significantly sacrificing performance.
3. Recommend ways to visualize model outputs for non-technical stakeholders.
4. Discuss potential trade-offs between interpretability and accuracy.

Constraints

• Limited budget for implementing interpretability tools.
• The model must remain deployable without extensive retraining.