Ensemble stacking rockburst prediction model based on yeo-johnson, k-means smote, and optimal rockburst feature dimension determination

HIGHLIGHTS

• who: Lijun Sun from the SchoolWuhan University have published the research: Ensemble stacking rockburst prediction model based on Yeo-Johnson, K-means SMOTE, and optimal rockburst feature dimension determination, in the Journal: Scientific Reports Scientific Reports
• what: Three-quarters of the dataset is used as the training set to train the model, and the remaining 1/4 is used as the test set to evaluate the reliability and generalization ability of the model. In the model training process, grid search with cross-validation is used to obtain the optimal parameters with the highest accuracy.
 



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