We show that in the context of classification the property of source and target distributions to be related by covariate shift may break down when the information content captured in the covariates is reduced, for instance by discretization of the covariates, dropping some of them, or by any transformation of the covariates even if it is domain-invariant. The consequences of this observation for class prior estimation under covariate shift are discussed. A probing algorithm as alternative approach to class prior estimation under covariate shift is proposed.

翻译：我们显示,在分类方面,由于共变式转移而关联的源属性和目标分布属性,当共变式中收集的信息内容减少时,可能会分解,例如,通过分解共变式,降低其中一些内容,或者即使共变式是域变量,也通过共变式的任何转变。讨论这一观察对在共变式转移下先前估算类别的后果。提出了一种验证算法,作为在共变式转移下对类别进行先前估算的替代方法。