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Given a supervised machine learning problem where the training set has beensubject to a known sampling bias, how can a model be trained to fit theoriginal dataset? We achieve this through the Bayesian inference framework byaltering the posterior distribution to account for the sampling function. Wethen apply this solution to binary logistic regression, and discuss scenarioswhere a dataset might be subject to intentional sample bias such as labelimbalance. This technique is widely applicable for statistical inference on bigdata, from the medical sciences to image recognition to marketing. Familiaritywith it will give the practitioner tools to improve their inference pipelinefrom data collection to model selection.

Sampling Bias Correction for Supervised Machine Learning: A Bayesian Inference Approach with Practical Applications