We argue that for analysis of Positive Unlabeled (PU) data under Selected Completely At Random (SCAR) assumption it is fruitful to view the problem as fitting of misspecified model to the data. Namely, we show that the results on misspecified fit imply that in the case when posterior probability of the response is modelled by logistic regression, fitting the logistic regression to the observable PU data which {\it does not} follow this model, still yields the vector of estimated parameters approximately colinear with the true vector of parameters. This observation together with choosing the intercept of the classifier based on optimisation of analogue of F1 measure yields a classifier which performs on par or better than its competitors on several real data sets considered.

翻译：我们论证，在选定完全随机缺失假设下分析正样本无标签数据时，将该问题视为对数据拟合错误设定的模型是有益的。具体而言，我们证明错误设定拟合的结果表明：当响应变量的后验概率由逻辑回归建模时，对可观测的正样本无标签数据（{\it 不}遵循该模型）进行逻辑回归拟合，仍能得到与真实参数向量近似共线的估计参数向量。这一观察结果与基于F1度量类似指标优化来选择分类器截距相结合，能够在所考虑的多个真实数据集上产生性能与竞争对手相当或更优的分类器。