Fig. 3

Feature importance evaluation using SHAP values and CC clustering. (a) Top ten most important features for the logistic regression with L1 regularisation into sensitive/tolerant categories. (b) Ten most important features for the random forest regression of MIC values. (c) Ten most important features for the random forest regression using log2 transformed MIC values.