Context

TechSolutions recently deployed a logistic regression model to predict customer churn for its subscription service. Over the last quarter, the model has shown significant fluctuations in performance metrics, raising concerns about its reliability. The model was trained on historical customer data and is expected to provide actionable insights to the marketing team.

Current Performance

The Problem

The model's performance has been inconsistent, with a notable decline in precision and recall in Month 2, impacting the marketing team's ability to target at-risk customers effectively. The VP of Customer Retention is concerned about the model's robustness and its ability to generalize to new data.

Requirements

1. Analyze the performance metrics over the three months and identify trends or anomalies.
2. Propose potential causes for the fluctuations in model performance.
3. Recommend validation techniques to ensure model reliability going forward.
4. Suggest strategies for improving model robustness and performance.

Constraints

• Limited resources for extensive retraining or feature engineering.
• The model must maintain a precision of at least 75% to be actionable for marketing campaigns.