We also quantified the relative impact of each parameter on PCE predictions using feature importance calculated from RF. The feature importance of each dimension of vectors was calculated (Table ) and then aggregated in the raw 248 columns in the dataset (Table ). The results show that, as in the case of SHAP, perovskite materials and processes were the top-ranked features (Fig. ). Furthermore, the feature importance of the material and process condition of each functional layer was summarized (Table ). The information on the perovskite layer contributed about 63% to the prediction of PCE, especially the process conditions contributed 41%. Information on the ETL and HTL layers also contributed about 12% each to the prediction. The substrate and back contact were rated as less critical because these layers contribute neither to carrier generation nor transport. Feature importance quantitatively evaluated the relative importance of material and process information, showing the significance of considering process conditions for experimental material development and machine-learning prediction.