Heteroscedastic machine learning models exist , but these do not necessarily allow the incorporation of two separate noise factors, as the variation in noise is dependent on the variable in heteroscedastic models. Conversely, multi-fidelity active machine learning strategies allow to employ widely abundant low-quality data for an accurate pretraining of the active machine learning model . These methods have been developed based on simulated "experimental" data only, but are very promising for improving the performance of active machine learning tools when applied to real experimental data.