topprismdata

Use when starting a new ML task that structurally resembles a known competition (recommendation ↔ retail, demand ↔ supply, churn ↔ fraud…

CV-LB Gap Acknowledgment: CV improvement does NOT equal LB improvement.

Day-specific (direct) multi-step time series forecasting.

Process approved local Office, PDF, image, Markdown, and related documents by evidence, version family, and formal admission rules.

Avoid applying domain knowledge constraints that hurt ML model performance.

Diagnose when ensemble/stacking will fail due to high model correlation.

Use when you have run 3+ feature engineering experiments with no improvement, or when distance to theoretical upper bound is <15pp.

Manage explicit source approval, exclusion, readmission, retry, and batch validation for Topprismwiki.

Use when: (1) Considering switching from CPU to GPU for a Kaggle competition, (2) GPU quota is running low and you need to prioritize…

Apply GSD Core's loop-engineering methodology to ML/data-science tasks.

Use when: (1) You have limited daily submission quota and 50+ skill files to explore, (2) Most Kaggle experiments cost 1 quota each but…

Guides end-to-end Kaggle competition workflows from RAG knowledge base setup and feature engineering to model ensembling and submission…