Data mining, KDD and Risk - Data Leaders Brief

Using Empirical Bayes to approximate posteriors for large "black box" estimators

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

These estimates can be useful to make risk-adjusted decisions and explore-exploit trade-offs, or to find situations where the underlying regression method is particularly good or bad. For example, we could use a relatively coarse generalization model for $t$ and rely on calibration to memorize item-specific information.

KDD

KDD Testing Machine Learning Modeling

Data Leaders Brief

Using Empirical Bayes to approximate posteriors for large "black box" estimators

Webinars

Stay Connected