an-oracle-ceiling-is-not-headroom-yet
SolidUse at hypothesis generation, study design and implementation once a submission exists and you are choosing where the remaining hours go -- in particular when a lever looks worth building because you worked out what it would pay if its setting were chosen perfectly for every row, or when the distance between your metric value and a published number for this dataset and metric looks like room to improve. Covers computing that best-possible value as an explicit ceiling, inverting it into the correlation a real predictor would need before it clears your ship bar, measuring the correlation your inputs actually carry, and checking that a published number you are chasing was computed the way your submission will be scored.
Install
Quality Score: 82/100
Skill Content
Details
- Author
- tangxiangru
- Repository
- tangxiangru/AutoR
- Created
- 6 months ago
- Last Updated
- 2 weeks ago
- Language
- Python
- License
- NOASSERTION
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
orchestrate-evidence-retrieval-expert
Design and defend the evidence column. Use when building retrieval or considering a ranker change. Covers the ceiling analysis that tells you whether a gain is possible at all, and why the fashionable option often loses.
calibrate-the-level-on-the-window-you-cannot-score
Use at implementation and afterwards whenever the rows you will be scored on lie outside every window you can check against truth — a forecast horizon that starts where the supplied history ends, a later time period, a different site, batch or cohort, a test split whose label column has been removed — and your only bias check was run on a backtest fold or a random validation split. Covers why "my predictions are unbiased" is a statement about the folds and not about the graded rows, how to measure the overall level of your predictions on the graded rows with no labels at all, why the level ratio is only the alarm and a metric scan is the number, what to do when the two windows disagree, and when a low forecast is correct rather than broken.
a-second-model-family-before-a-fifth-sample
Use at literature survey, study design, implementation and experimentation when the predictions come from running a pretrained checkpoint you picked off the shelf over each row — a language model that reads the text and answers, an encoder, any released artifact — rather than from fitting a model on the training rows, and especially when the next thing you planned is another sample, seed, temperature or voter from the checkpoint you already downloaded. Covers treating the set of checkpoints as an experimental axis with a deadline of its own, the best-single / oracle / best-vote measurement on your own labelled rows that decides whether to buy a better aggregator or a different model, and why a ceiling computed from your own predictions bounds your shortlist rather than the task.