fig5

Decoding donor/acceptor hierarchy in DAD triads via fragment-centric machine learning

Figure 5. Features’ marginal effect analysis for ΔETT: PDP (red) and ICE (blue) plots of FODs under XGBoost, where (A-H) correspond to features HD, LD, H1D, L1D, HA, LA, H1A, L1A, respectively. PDP: Partial dependence plots; ICE: individual conditional expectation; FODs: fragment orbital descriptors; XGBoost: eXtreme gradient boosting.

Journal of Materials Informatics
ISSN 2770-372X (Online)
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