I recently came across the thoughtful article “On Moving from Statistics to Machine Learning, the Final Stage of Grief”. It makes some good points, and is worth the read. However, it also reminded me of the unexamined claim “data science is statistics done wrong.” Frankly this is not the case, […]
In the previous article in this series, we showed that common ensemble models like random forest and gradient boosting are uncalibrated: they are not guaranteed to estimate aggregates or rollups of the data in an unbiased way. However, they can be preferable to calibrated models such as linear or generalized […]
Continuing our series of reading out loud from a single page of a statistics book we look at page 224 of the 1972 Dover edition of Leonard J. Savage’s “The Foundations of Statistics.” On this page we are treated to an example attributed to Leo A. Goodman in 1953 that […]