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Thought this was interesting. Across 160 teams of researchers, just about all failed to make good life outcome predictions on things like GPA, evictions, layoffs, and others. Data followed 4.5k families across 15 years, with 13k features (varied over time). Haven't looked at it directly yet, but will be turning the docs and data inside out... In the meantime, authors claim this as showing the limits of ML. Oh, and it's published in PNAS, so you know there's some big publication energy there.
https://www.pnas.org/content/117/15/8398
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Lambda should be the value that once applied most closely represents a normal distribution.
There are 2 methods for calculating lambda, the “guerrero” and “loglik” method (reference is the BoxCox.lambda function in the forecast package). In Guerrero’s (1993) method lambda minimizes the coefficient of variation for subseries of data. In the “loglik” method, the value of lambda is chosen to maximize the profile log likelihood of a linear model fitted to data.
Oh I remember this from my college days.. y’all are actually using it?