## 3 Results and Discussion
### Data Imbalance Issue  
The LightGBM algorithm will use the nine features screened in Section 3.2 as input parameters to predict the phase of the alloy. In order to solve the problem of lack of accuracy in BCC+FCC phase prediction caused by alloy phase class imbalance, we used SMOTE, SVM-SMOTE, Borderline-SMOTE, ADASYN, and other oversampling methods to process the dataset. It is worth mentioning that when using 10-fold to evaluate the algorithm's accuracy, to prevent problems such as overestimation of prediction accuracy, overfitting, and so on, we only oversampled the training set of the ten subsets generated by 10-fold, and the test set was used for testing only. This operation can effectively prevent data leakage, model overfitting, etc. In the hyperparameter optimization of LightGBM using Bayesian optimization methods, the accuracy of the algorithm corresponding to different hyperparameter combinations was also evaluated using the above method. Further, we used cost sensitivity in dataset training to enhance the model's penalty for misclassification of minority classes and thus improve the classification accuracy of minority-class samples. Four oversampling methods were used for dataset training, and the confusion matrix of the training results was evaluated using a 10-fold. As shown in Figure 5, the prediction accuracy of the LightGBM algorithm processed by each of the four oversampling methods is very close, which may be due to the fact that these methods are based on the SMOTE method. However, by observing the classification effects of a few classes, it can be found that after ADASYN oversampling, the HCP phase has the highest classification accuracy of 92.11%, and the overall classifier has 95.44% classification accuracy. Although several oversampling methods combined with cost-sensitive methods have achieved good prediction results, the LightGBM algorithm trained by the ADASYN method is undoubtedly better for the classification of a few classes. Also, the classification accuracy of the overall alloy phase of the model trained with the training set processed with ADASYNis good among the four types of SMOTE oversampling. Further, the LightGBM model is constructed by combining the ADASYN method with the cost-sensitive method, which is further evaluated using the evaluation metrics precision, F1-score, and recall. Finally, with 10-fold verification, precision reached 0.9575, F1-score reached 0.9543, and recall reached 0.9544. This shows that by using the LightGBM algorithm based on EL, the ADASYN oversampling method, and cost-sensitive learning, the classification accuracy of BCC+FCC for minority-class samples is successfully improved from 75.68% to 86.49%, alleviating the problem of poor classification accuracy of minority classes caused by data imbalance. In order to validate the predictive effectiveness of the model, we tested the model using data from outside the LightGBM model training dataset. The test results are shown in Table 2, which demonstrates the good prediction of the model. Figure 3: Pair-plot matrices show the correlation between nine features that are linked with the phase selection in 1009 alloys. The diagonal plots show the distribution of BCC, FCC, HCP, and BCC+FCC with different values of the nine features. The off-diagonal plots show the distribution between any two of the nine features with different phases. The subgraph involved in the red box is the distribution relationship diagram between feature Col and other features. Figure 4: Relationship between features and alloy phases analyzed by the SHAP method. Figure 5: Confusion matrix of combination with cost-sensitive LightGBM for (**a**) SMOTE, (**b**) ADASYN, (**c**) Borderline-SMOTE, and (**d**) SVM-SMOTE oversampling methods.