Amey-Thakur Kaggle competition workflow
Competitions reward a specific discipline: a validation scheme you can trust, a strong baseline fast, then relentless iteration measured against that validation, not the public leaderboard. Most competitors lose rank by overfitting the leaderboard or skipping the foundations.
Method
- Understand the problem and metric first. Read the data, the target, and above all the evaluation metric: it dictates everything (a metric like log-loss, AUC, or RMSLE changes your loss, your thresholding, and what to optimize). Reproduce the metric locally so you can measure yourself.
- Build a trustworthy validation scheme before anything else. This is the single most important step. A cross-validation setup that mirrors how train and test differ (grouped by entity, split by time, stratified for imbalance) is your private measure of progress (see cross-validation, leaderboard-strategy). Everything after is measured against it; get it wrong and you optimize noise.
- Get a simple baseline submitted fast. A minimal pipeline (basic features, a gradient-boosted model with defaults, your CV) end to end, submitted early. It confirms the pipeline works, calibrates local-CV to leaderboard, and gives a number to beat (see ml-baselines). Do not spend week one on a fancy model with no baseline.
- Iterate on features, measured by CV. On tabular problems, feature engineering drives the gains (see feature-engineering-tabular); on images/text, it is architecture and augmentation. Add improvements one at a time, keep what improves cross-validation (not public LB), and log every experiment so you know what worked (see experiment-tracking).
- Tune and then ensemble, late. Hyperparameter tuning gives modest gains (see gradient-boosting-tuning); ensembling diverse models gives more (see model-ensembling). Do both after features are strong, not before: ensembling weak models is a weak ensemble.
- Manage the endgame deliberately. Watch for overfitting the public leaderboard, trust your CV for final submission selection (pick your two by CV, not by public LB rank; see leaderboard-strategy), and read the forums and public notebooks for tricks and leaks you missed.
Boundaries
- Competition skill and production ML differ: competitions reward squeezing the metric with heavy ensembles that would be too slow and fragile to deploy. The validation, feature, and leakage discipline transfers; the giant stacked ensemble does not (see model-deployment).
- The public leaderboard is a small, sometimes adversarial slice; treating it as truth is the classic way to drop hundreds of ranks on the private set (see leaderboard-strategy).
- Time is the real constraint; a good baseline plus honest CV plus a few strong iterations beats an unfinished grand plan. Prioritize by expected gain (see prioritize-tasks).