calibrate-the-level-on-the-window-you-cannot-score
SolidUse 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.
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
a-priced-bias-is-a-work-item-not-a-caveat
Use at implementation, experimentation and analysis on a task graded by an error metric over a predictions file, when a diagnostic you ran after freezing your design says the numbers you are about to ship are biased - too high, too low, on the wrong scale, in the wrong units - and a pre-registered decision rule is the reason you are recording it rather than fixing it. Covers the one test that separates a forbidden search over candidates from an ordinary bias correction, where the correction may be estimated, the held-out check that has to pass before you apply it, and how to ship the corrected file while still reporting the frozen verdict.
add-the-baseline-back-on-the-split-you-cannot-score
Use at study design, implementation and every write thereafter, whenever the value you submit is assembled from parts - a fitted baseline plus a model's residual, a level plus a shape, a de-trended prediction that has to be re-trended, any inverse transform - and the validation arrays and the graded arrays are produced by separate calls. Covers assembling every split through one function, using the baseline you already fitted as a label-free reference vector on the graded split, why row count, header, dtype and finiteness cannot see this class of error, and putting the gate inside the writer rather than in a script somebody has to remember to run.
a-model-you-can-audit-is-not-a-model-that-scores
Use at study design and implementation when choosing between a method you can validate quickly and a stronger one you are not sure you can afford. Covers pricing the expensive method with a measurement instead of an impression, the go/no-go that has to be written before the clock is spent, and why the safe choice is only safe on the axes nobody is grading.