This paper addresses the problem of making statistical inference about a population that can only be identified through classifier predictions. The problem is motivated by scientific studies in which human labels of a population are replaced by a classifier. For downstream analysis of the population based on classifier predictions to be sound, the predictions must generalize equally across experimental conditions. In this paper, we formalize the task of statistical inference using classifier predictions, and propose bootstrap procedures to allow inference with a generalizable classifier. We demonstrate the performance of our methods through extensive simulations and a case study with live cell imaging data.

翻译：本文论述对只能通过分类预测才能查明的人口进行统计推断的问题; 这个问题的起因是科学研究,在科学研究中,人口的人的标签被一个分类者所取代; 为了根据分类预测对下游人口进行分析,必须根据可靠的分类者预测,对各试验条件进行平均的预测; 在本文件中,我们正式确定使用分类预测进行统计推断的任务,并提议靴套程序,以便与一个通用分类者进行推断; 我们通过广泛的模拟和用活细胞成像数据进行案例研究,展示我们方法的绩效。