Predicting the Quality of Transmission (QoT) of a candidate lightpath prior to its establishment plays a pivotal role for an effective design and management of optical networks. In the last few years, supervised Machine Learning (ML) techniques have been advocated as promising approaches for QoT estimation, but to ensure the effectiveness of their training phase, a large amount of samples (training set) must be provided to the learning algorithm. Unfortunately, the collection of training samples is often hindered by practical issues (e.g., lack of dedicated telemetry equipment in every network node) or is too costly to permit the acquisition of large datasets.