Tag:machine-learning
All the articles with the tag "machine-learning".
Perfect Precision, Zero Recall, and No Curve to Trade Along
A document check cleared every forgery in a labelled set. The failure was the level of analysis, not the threshold: a seeded run shows the container detector reaching exactly two operating points, AUC 0.500, and shows what a genuine-only calibration can and cannot buy.
The Constant Nobody Could Re-Measure
A magic number survives because there is nothing to re-measure it against. The calibration set is usually already in a log you throw away, the same data sets the noise floor that makes any result readable, and the estimator you fix is rarely where the error was.
The Forecast Metric That Hides the Failure
A volume weighted error of 7.6 percent and a net bias of 0.2 percent can describe a forecast that loses to a seasonal naive on 77 of 116 slow moving series. The same weighting is applied twice more upstream.
Calendar Features for TabPFN Time Series: Geometry, Encodings, and an Open Edge Case
TabPFN-TS turns forecasting into a table-plus-features problem. A 24-hour edge case shows why geometry, checkpoint compatibility, and release evidence are separate gates.
How Far Should You Tune LightGBM? A Budget for the Last Fraction of a Point
LightGBM tuning can win, but only a deployment-faithful baseline and nested validation can show whether the last fraction of a point pays for its search.
Are Tabular Foundation Models Worth the Hype? My Bet Is Yes - but Not for the Reason You Think
Tabular foundation models are a real shift, but the durable bet is about information sets, task breadth, and synthetic priors - not easy benchmark wins.