a-scoreable-file-in-the-first-hour
SolidUse at the first stage of a run whose deliverable is a predictions file, and again at every stage when one still does not exist. Covers why a trivial submission written early dominates a good one written late, what the first version should contain, and how to improve it in place without ever leaving it invalid.
Install
Quality Score: 85/100
Skill Content
Details
- Author
- tangxiangru
- Repository
- tangxiangru/AutoR
- Created
- 5 months ago
- Last Updated
- today
- Language
- Python
- License
- NOASSERTION
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
chemistry-reproduce-the-scoring-path-before-you-replace-it
Use at implementation, experimentation and analysis when you are reproducing a published benchmark number and the source's scoring path is one you can read — which rows are scored, in what order, how many the loader drops, which epoch is reported, how tasks are pooled, over how many seeds. Covers implementing that path exactly before improving it, the one-row-per-step ladder from the published rule down to your own honest estimate, and why one un-replicated step makes the reproduction gap you report uninterpretable.
machine-learning
Builds, ships and operates predictive models - framing the problem, constructing features and splits, training reproducibly, serving, and knowing when to retrain. Use when deciding whether something is an ML problem, defining a target and the baseline it must beat, designing splits for time-ordered or grouped data, choosing a model family and a tuning budget, reviewing training code, or diagnosing a model that validated well and disappoints in production - train/serve skew, drift, retraining triggers. Covers leakage prevention, imbalance, encoding and missingness. Not for whether an improvement is real (statistics), stack selection or LLM, RAG and agent design (ai-engineering), or pipelines and warehouses (data-engineering). Ships an offline AST auditor (scripts/ml_check.py).
machine-learning
Builds, ships and operates predictive models - framing the problem, constructing features and splits, training reproducibly, serving, and knowing when to retrain. Use when deciding whether something is an ML problem, defining a target and the baseline it must beat, designing splits for time-ordered or grouped data, choosing a model family and a tuning budget, reviewing training code, or diagnosing a model that validated well and disappoints in production - train/serve skew, drift, retraining triggers. Covers leakage prevention, imbalance, encoding and missingness. Not for whether an improvement is real (statistics), stack selection or LLM, RAG and agent design (ai-engineering), or pipelines and warehouses (data-engineering). Ships an offline AST auditor (scripts/ml_check.py).