To understand the intuition behind this selectivity drop, we use the SHAP library of interpretation models to draw relationships between the descriptors and the predicted ambientpressure selectivity. This code library is based on the calculation of Shapley values 42 that measure the contribution of each descriptor to the prediction to locally interpret our ML model. In game theory, the Shapley value is used to equally distribute a bounty according to the feature importance (see Figure and). This mean value corresponds to the average magnitude of the contribution to the predicted value, which is a measure of the influence of the feature on the model output.