  Labelling data is a major practical bottleneck in training and testing
classifiers. Given a collection of unlabelled data points, we address how to
select which subset to label to best estimate test metrics such as accuracy,
$F_1$ score or micro/macro $F_1$. We consider two sampling based approaches,
namely the well-known Importance Sampling and we introduce a novel application
of Poisson Sampling. For both approaches we derive the minimal error sampling
distributions and how to approximate and use them to form estimators and
confidence intervals. We show that Poisson Sampling outperforms Importance
Sampling both theoretically and experimentally.
