, which can be costly and inefficient. In this study, extensive historical data were collected from literature, and a dataset consisted of 2365 sets of data involving 14 input variables and 1 output variable was constructed after careful selection. Subsequently, 6 machine learning algorithms were trained, tuned, and compared. The optimized gradient boosting regressor model with RMSE of 7.874 % and R2 of 0.9063 for the test set was deemed robust and reliable. Leaching time was identified as the most influential factor on the extraction efficiency of rare earth oxides, followed by the concentrat
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