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Quality-of-Transmission (QoT) regression of unestablished lightpaths is a fundamental problem in Machine Learning applied to optical networks. Even though this problem is well-investigated in current literature, many state-of-the-art approaches either predict point-estimates of the QoT or make simplifying assumptions about the QoT distribution. Because of this, during lightpath deployment, an operator might take either overly-aggressive or overly-conservative decisions due to biased predictions. In this paper, we leverage state-of-the-art Gradient Boosting Decision Tree (GBDT) models and recent advances in uncertainty calibration to perform QoT probabilistic regression for unestablished lightpaths. Calibration of a regression model allows for an accurate modeling of the QoT Cumulative Distribution Function (CDF) without any prior assumption on the QoT distribution. In our illustrative experimental results, we …