topprismdata

Correct implementation of adversarial validation for Kaggle competitions.

Agent Nurture Framework: a systematic methodology for training AI agents from novice to expert through conversational knowledge…

Use when: (1) Starting a long-running experiment that may span multiple sessions/disconnects, (2) Resuming work after a break and needing…

Use when encountering a new ML technique not covered by existing 43+ skills, when needing citations for a paper/report, when comparing…

AutoGluon-First Strategy: Always run AutoGluon `best_quality` preset as the first step in any tabular ML competition (5-15 min baseline).

AutoGluon preset selection strategy: when to use medium/good/high/best_quality, when EDA is unnecessary, and how to tune within a preset.

AutoGluon TimeSeriesPredictor: special API and presets for time series forecasting (different from TabularPredictor).

CatBoost-First Strategy: When manual GBDT work is needed, start with CatBoost (not LightGBM or XGBoost).

Extract reusable knowledge from work sessions into new Claude Code skills.

Use when: (1) Entering a Kaggle Code Competition that requires notebooks with no internet access, (2) Need to fork a public baseline that…

Use when: (1) You need to rapidly iterate across MANY competing approaches in a single Kaggle competition (NeuroGolf-style: 7+ different…

Use when: (1) a new submission scores worse than baseline and the reason is unclear, (2) multiple changes were made simultaneously (new…