xCr1–x體系RHEAs的實驗結果顯示，RF和ANN的預測精度更高，精準預測了11個SS和3個SS+IM合金的相組成。采用了SHAP（SHapley Additive exPlanations）模型來解釋精度最高的 ANN 模型，并研究每個特征對相形成的貢獻。5個特征的重要性順序是混合焓（ΔHmix）、原子尺寸差（δ）、價電子濃度（VEC）、混合熵（ΔSmix）和電負性差（Δχ），其中ΔHmix的平均SHAP值大約是Δχ的5倍，是ΔSmix的4倍。較大的ΔHmix、較小的δ和VEC可能有助于RHEA中固溶體的形成。;Five machine learning (ML) approaches, i.e. K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Artificial Neural Network (ANN) were used to classify and to predict the combination of phases, i.e. solid solutions (SS) and mixed solid solution and intermetallic (SS+IM) in refractory high-entropy alloys (RHEAs). Five input characteristic phase predicting parameters and 139 RHEAs were selected to train these models. Results show that ANN model has the highest accuracy of 90.72%. Experimental results of 9 quaternary and (TiVTa)xCr1–x RHEAs verify the accuracy of prediction and indicate that RF and ANN can predict more accurately, successfully predicting 11 SS and 3 SS+IM. SHAP (SHapley Additive exPlanations) model was used to interpret the ANN model which exhibits the highest accuracy and to investigate the contribution of each feature to phase formation. The order of importance of five features is enthalpy of mixing (ΔHmix), atomic size difference (δ), valence electron concentration (VEC), entropy of mixing (ΔSmix), and electronegativity difference (Δχ), where the mean SHAP value of ΔHmix is approximately 5 times higher than that of ?χ and 4 times higher than that of ΔSmix. Less negative ΔHmix, smaller δ and VEC may contribute to the formation of SS in RHEAs."/>