Regression with noisy input features shrinks predictions toward the mean by a factor 1 divided by (1 plus the squared ratio of noise to signal spread), and this bias persists regardless of training sample size, label accuracy, or sample distribution.
The Stellar Content of Active Galaxies
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abstract
We present results of a long-slit spectroscopic study of 39 active and 3 normal galaxies. Stellar absorption features, continuum colors and their radial variations are analyzed in an effort to characterize the stellar population in these galaxies and detect the presence of a featureless continuum underlying the starlight spectral component. Spatial variations of the equivalent widths of conspicuous absorption lines and continuum colors are detected in most galaxies. Star-forming rings, in particular, leave clear fingerprints in the equivalent widths and color profiles. We find that the stellar populations in the inner regions of active galaxies present a variety of characteristics, and cannot be represented by a single starlight template. Dilution of the stellar lines by an underlying featureless continuum is detected in most broad-lined objects, but little or no dilution is found for the most of the 20 type 2 Seyferts in the sample. Color gradients are also ubiquitous. In particular, all but one of the observed Seyfert 2s are redder at the nucleus than in its immediate vicinity. Possible consequences of these findings are outlined.
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Why Machine Learning Models Systematically Underestimate Extreme Values
Regression with noisy input features shrinks predictions toward the mean by a factor 1 divided by (1 plus the squared ratio of noise to signal spread), and this bias persists regardless of training sample size, label accuracy, or sample distribution.