calibrate-the-level-on-the-window-you-cannot-score

Solid

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.

AI & Automation 805 stars 25 forks Updated 2 weeks ago NOASSERTION

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# Your bias check was run somewhere else Two sentences sound identical and are not: - *my predictions are unbiased on the folds I can score* - *my predictions are unbiased on the rows that will be graded* The first is a measurement. The second is an extrapolation from it, and it is only safe when the graded rows are exchangeable with the folds. When the graded split is a **later period, a different site, a held-out batch, or a horizon that starts where the supplied history ends**, they are not exchangeable — that is the whole reason the split was cut that way — and the extrapolation is usually the largest un-audited assumption in the run. The check you can run is the first sentence. The number you are paid on is the second. This skill is about closing that distance without labels. ## Why the level, specifically For any error sensitive to scale — absolute error, squared error, a scaled or percentage error, anything divided by a per-unit denominator — a single global multiplier on all your predictions is: - **one degree of freedom**, so it cannot meaningfully overfit; - **flat near its optimum**, so being roughly right captures nearly all of it; - **the only correction you can apply to a region you cannot score at all**, because it needs no per-row information. That makes it the highest-leverage-per-minute knob you own late in a run, and the one most reliably declared finished early — because the check that closed it was cheap and returned a comforting answer. ## 1. ...

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Author
tangxiangru
Repository
tangxiangru/AutoR
Created
6 months ago
Last Updated
2 weeks ago
Language
Python
License
NOASSERTION

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