We demonstrate ease.ml\/snoopy, a data analytics system that performs feasibility analysis for machine learning (ML) applications before they are developed. Given a performance target of an ML application (e.g., accuracy above 0.95), ease.ml\/snoopy provides a decisive answer to ML developers regarding whether the target is achievable or not. We formulate the feasibility analysis problem as an instance of Bayes error estimation. That is, for a data (distribution) on which the ML application should be performed, ease.ml\/snoopy provides an estimate of the Bayes error \u2013 the minimum error rate that can be achieved by any classifier. It is well-known that estimating the Bayes error is a notoriously hard task. In ease.ml\/snoopy we explore and employ estimators based on the combination of (1) nearest neighbor (NN) classifiers and (2) pre-trained feature transformations. To the best of our knowledge,
\nthis is the first work on Bayes error estimation that combines (1) and (2). In today\u2019s cost-driven business world, feasibility of an ML project is an ideal piece of information for ML application developers \u2013 ease.ml\/snoopy plays the role of a reliable \u201cconsultant\u201d.<\/p>\n","protected":false},"excerpt":{"rendered":"
We demonstrate ease.ml\/snoopy, a data analytics system that performs feasibility analysis for machine learning (ML) applications before they are developed. Given a performance target of an ML application (e.g., accuracy above 0.95), ease.ml\/snoopy provides a decisive answer to ML developers regarding whether the target is achievable or not. 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