Context

At ShopLens, you built a binary classifier for a Kaggle-style product return prediction challenge. The leaderboard metric is F1 score on the positive class, and your current model is competitive but plateaued near the top 20%.

You have one additional week before final submission. The goal is not to rebuild the project from scratch, but to use model evaluation and error analysis to identify the highest-leverage techniques for improving F1.

Current Performance

The Problem

Your model has strong precision but weaker recall, which is limiting F1. Error analysis suggests the model underperforms on minority subgroups and likely uses a suboptimal decision threshold. You need to decide what advanced techniques would most likely improve F1 within one week.

Requirements

1. Interpret what the current metrics imply about model behavior.
2. Identify the most likely reasons F1 is lagging despite decent AUC-ROC.
3. Recommend the highest-impact techniques you would implement in one week.
4. Explain how you would validate that improvements are real and not leaderboard overfitting.
5. Discuss tradeoffs between threshold tuning, feature work, ensembling, and calibration.

Constraints

• One week of additional work
• Kaggle-style blind test set
• Final metric is F1, not AUC or log loss
• Limited compute: no large-scale deep learning retraining