Machine learning-based unified models for predicting drug clearance from pharmacokinetic animal and study design variables
Fig 5
(A). SHAP summary plot, Figure (B): SHAP bar plot, (C) – (D): Representative SHAP waterfall plots depicting feature (study design variables and molecular descriptors) contributions to individual predictions.
It shows how each attribute contributes positively or negatively to predicting the target values. E[f(X)] represents the base value which is the average model output from the SHAP implementation and functions as the reference point. Representative datasets with actual values, highlighted in green boxes, and their predictions represented as f(X). Note that, in this study the unit of clearance value is considered in mL/min/kg, and so the predicted value may find different from the one displayed in green boxes.