How many labeled examples are needed to estimate a classifier's performance on a new dataset? We study the case where data is plentiful, but labels are expensive. We show that by making a few reasonable assumptions on the structure of the data, it is possible to estimate performance curves, with confidence bounds, using a small number of ground truth labels. Our approach, which we call Semisupervised Performance Evaluation (SPE), is based on a generative model for the classifier's confidence scores. In addition to estimating the performance of classifiers on new datasets, SPE can be used to recalibrate a classifier by re-estimating the class-conditional confidence distributions.
We address the problem of estimating the Fα-measure of a given model as accurately as possible on a fixed labeling budget. This problem occurs whenever an estimate cannot be obtained from held-out training data; for instance, when data that have been used to train the model are held back for reasons of privacy or do not reflect the test distribution. In this case, new test instances have to be drawn and labeled at a cost. An active estimation procedure selects instances according to an instrumental sampling distribution. An analysis of the sources of estimation error leads to an optimal sampling distribution that minimizes estimator variance. We explore conditions under which active estimates of Fα-measures are more accurate than estimates based on instances sampled from the test distribution. Machine learning often relies on costly labeled data, and this impedes its application to new classification and information extraction problems. This has motivated the development of methods for leveraging abundant prior knowledge about these problems, including methods for lightly supervised learning using model expectation constraints. Building on this work, we envision an interactive training paradigm in which practitioners perform evaluation, analyze errors, and provide and refine expectation constraints in a closed loop. In this paper, we focus on several key subproblems in this paradigm that can be cast as selecting a representative sample of the unlabeled data for the practitioner to inspect. To address these problems, we propose stratified sampling methods that use model expectations as a proxy for latent output variables. In classification and sequence labeling experiments, these sampling strategies reduce accuracy evaluation effort by as much as 53 , provide more reliable estimates of @math for rare labels, and aid in the specification and refinement of constraints. Deploying a classifier to large-scale systems such as the web requires careful feature design and performance evaluation. Evaluation is particularly challenging because these large collections frequently change. In this paper we adapt stratified sampling techniques to evaluate the precision of classifiers deployed in large-scale systems. We investigate different types of stratification strategies, and then we derive a new online sampling algorithm that incrementally approximates the theoretical optimal disproportionate sampling strategy. In experiments, the proposed algorithm significantly outperforms both simple random sampling as well as other types of stratified sampling, with an average reduction of about 20 in labeling effort to reach the same confidence and interval-bounds on precision We address the problem of evaluating the risk of a given model accurately at minimal labeling costs. This problem occurs in situations in which risk estimates cannot be obtained from held-out training data, because the training data are unavailable or do not reflect the desired test distribution. We study active risk estimation processes in which instances are actively selected by a sampling process from a pool of unlabeled test instances and their labels are queried. We derive the sampling distribution that minimizes the estimation error of the active risk estimator when used to select instances from the pool. An analysis of the distribution that governs the estimator leads to confidence intervals. We empirically study conditions under which the active risk estimate is more accurate than a standard risk estimate that draws equally many instances from the test distribution.