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> The efficiency of an estimator is a different from its bias. I think the comment is drawing a parallel to variance (better efficiency = lower variance). Stil
by ianhorn 6y ago
> The efficiency of an estimator is a different from its bias.
I think the comment is drawing a parallel to variance (better efficiency = lower variance). Still not exactly the same, I think, but pretty damn similar.
- xapata 6y agoThey're related in that less complex models will degrade more gracefully when making predictions on novel anomalies, and that in general model complexity drives the bias-variance trade-off. But, erring on the side of efficiency in this discussion is more like over-fitting, which implies an overly complex model. It's making your model too good for one situation, such that it fails to generalize. You'd rather pull back on accuracy and choose a simpler model, in the hopes that it's more resilient to novel observations.