Towards a real-time unsupervised estimation of predictive model degradation
Tania Cerquitelli, Stefano Proto, Francesco Ventura, Daniele Apiletti, Elena Baralis · 2019
Automating predictive machine learning entails the capability of properly triggering the update of the trained models. To this aim, the degradation of predictive models has to be continuously evaluated over time to detect data distribution drifts between the original training set and the new data. Traditionally, prediction performance is used as a degradation metric. However, prediction quality indices require ground-truth class labels to be known for the newly classified data, making them unsuitable for real-time applications, as ground-truth labels might be totally absent or be available only later.