Although prediction accuracy is limited due to data degeneracy, an interpretation of the machine learning model is important for obtaining chemical insight. We performed SHAP analysis, which is beneficial for identifying the positive or negative effect of each variable . Here, the top 10 dimensions after sorting by the averaged SHAP value are highlighted in different colors depending on the layer (Fig. ). The 1st ranked dimension, the quenching condition assigned as perovskite process, showed low feature values (shown as blue points) distributing in the negative region of SHAP values and high feature values (red points) distributing in the positive region of SHAP values. This result suggests that the use of antisolvent as quenching media enhances PCE. This is consistent with the experimental finding , as explained in the introduction. The features related to HTL layer, 2nd and 7th in the ranking, showed a relationship that PCE tended to increase using PTAA rather than PEDOT:PSS. This result has also been experimentally found . The 3rd feature related to perovskite material indicates that single-cationic perovskites are inferior to mixed ones, consistent with experimental result . These SHAP interpretations afforded known chemical insights and confirmed the validity of the trained RF model consistent with experimental findings.