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In our experience of working with domain experts who are using today's AutoMLsystems, a common problem we encountered is what we call "unrealisticexpectations" -- when users are facing a very challenging task with a noisydata acquisition process, while being expected to achieve startlingly highaccuracy with machine learning (ML). Many of these are predestined to fail fromthe beginning. In traditional software engineering, this problem is addressedvia a feasibility study, an indispensable step before developing any softwaresystem. In this paper, we present Snoopy, with the goal of supporting datascientists and machine learning engineers performing a systematic andtheoretically founded feasibility study before building ML applications. Weapproach this problem by estimating the irreducible error of the underlyingtask, also known as the Bayes error rate (BER), which stems from data qualityissues in datasets used to train or evaluate ML model artifacts. We design apractical Bayes error estimator that is compared against baseline feasibilitystudy candidates on 6 datasets (with additional real and synthetic noise ofdifferent levels) in computer vision and natural language processing.Furthermore, by including our systematic feasibility study with additionalsignals into the iterative label cleaning process, we demonstrate in end-to-endexperiments how users are able to save substantial labeling time and monetaryefforts.

Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise