Improve Kaggle Classifier F1 Score | Dataford Interview Questions

Your question is Improve Kaggle Classifier F1 Score. Take a moment with it on the right.

Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).

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

Metric	Cross-Validation	Public Leaderboard	Holdout Error Slice
Precision	0.81	0.79	0.68 on rare categories
Recall	0.63	0.61	0.49 on low-history users
F1 Score	0.71	0.69	0.57 on cold-start segments
AUC-ROC	0.86	0.85	0.78 on rare categories
Positive Rate	18.4%	18.1%	24.7% in rare categories
Threshold	0.50	0.50	—

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

Interpret what the current metrics imply about model behavior.
Identify the most likely reasons F1 is lagging despite decent AUC-ROC.
Recommend the highest-impact techniques you would implement in one week.
Explain how you would validate that improvements are real and not leaderboard overfitting.
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