基于随机森林回归模型的煤胶质层指数预测

In order to improve the efficiency of coal quality detection, a machine learning regression model was developed to enhance the efficiency and accuracy of coal quality analysis. This model constructed a comprehensive dataset by analyzing key industrial parameters from 99 different coal samples, including moisture, ash content, volatile matter, sulfur content, bonding index, and Gieseler fluidity index. First, Pearson correlation analysis was used to identify features closely related to the Gieseler fluidity index, and recursive feature elimination with cross-validation (RFECV) was employed for further refinement of feature selection. Three machine learning models-Random Forest, Support Vector Machine, and Decision Tree Regression-were con-structed to predict the Gieseler fluidity index of coal. The model\'s hyperparameters were optimized through grid search and cross-validation techniques to ensure optimal performance. The final evaluation results of the model indicated that the Random Forest regression model achieved very high prediction accuracy, with a coefficient of determination (R2) of 0.969, a root mean squared error (RMSE) of 0.171, and a mean absolute error (MAE) of 0.108.