Entropic Scree: a new method to test whether your dirty data holds a real signal
Chocolate_Milk_Son · reddit · 2026-08-31
- A new tabular diagnostic tool, Entropic Scree, estimates for high-dimensional dirty datasets: whether the signal's informational volume can survive idiosyncratic noise, the signal-to-idiosyncratic ratio (SNR), intrinsic rank, decoupled variable sub-networks, and linear sufficiency for standard PCA.
- It evaluates a transformed mutual information metric instead of the linear variance, rank order, or Euclidean distance of PCA variants, relying less on parametric/distance assumptions.
- It also serves as a practical diagnostic of the From Garbage to Gold framework on when uncurated, error-prone data can directly yield accurate models.
- Preprint and GitHub are live; an R quick-start function is available, with Python and R packages coming.
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