Optimization in machine learning: application to soil organic carbon distribution prediction in China

Context Accurately predicting soil organic carbon (SOC) and quantifying its influencing factors are crucial for global ecological sustainable development. Aims This study developed a SOC prediction model using machine learning methods – random forest, support vector machine (SVM), and deep neural network – based on 191 soil samples (100 from karst regions and 91 from non-karst regions). Variable selection and hyperparameter optimization were applied to improve model performance and clarify the i