Supervised machine learning algorithms are utilized to predict undiscovered mineral resources by analyzing the correlation between geological data and mineral deposits. The scarcity of mineralization and the uncertainty arising from the selection of training samples also the accuracy and generalization of such algorithms. This study employed the adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), and extreme gradient boosting (XgBoost) algorithms to map the prospectivity of tungsten polymetallic mineral resources in the Nanling metallogenic belt. Firstly, the under-sampling and synthetic minority oversampling technique (SMOTE) methods were used to generate training datasets. Secondly, 50 groups of training datasets were generated using under-sampling, and another 50 groups of training datasets were generated using the SMOTE method. These datasets were used to separately train different boosting algorithms in order to assess the uncertainty associated with the selection of negative samples and the generation of positive samples. Finally, the risk–return analysis was used to mitigate uncertainty, and an enhanced prediction–area (P–A) plot was proposed to evaluate the performance. The results indicate that AdaBoost is the least affected by the selection of negative samples, followed by XgBoost. The SMOTE not only enhances the performance of AdaBoost and XgBoost algorithms but it also reduces the uncertainty related to the selection of negative samples and the generation of positive samples. In addition, the enhanced P–A plot can simultaneously account for both prediction accuracy and uncertainty, making it a potential tool for model evaluation. According to the results, eight potential areas with high return and low risk have been identified as priority areas for exploration. This research not only introduces a new method for mineral prospectivity mapping and uncertainty evaluation but also provides guidance for mineral exploration in this region.

利用监督机器学习算法通过分析地质数据和矿藏之间的相关性来预测未发现的矿产资源。矿化的稀缺性和训练样本选择带来的不确定性也影响了此类算法的准确性和泛化性。本研究采用自适应Boosting（AdaBoost）、梯度Boosting决策树（GBDT）和极限梯度Boosting（XgBoost）算法对南岭成矿带钨多金属矿产资源前景进行了绘制。首先，使用欠采样和合成少数过采样技术（SMOTE）方法来生成训练数据集。其次，使用欠采样生成50组训练数据集，并使用SMOTE方法生成另外50组训练数据集。这些数据集用于单独训练不同的增强算法，以评估与负样本选择和正样本生成相关的不确定性。最后，使用风险回报分析来减轻不确定性，并提出了增强的预测面积（P-A）图来评估性能。结果表明，AdaBoost 受负样本选择影响最小，其次是 XgBoost。 SMOTE不仅增强了AdaBoost和XgBoost算法的性能，而且还减少了与负样本选择和正样本生成相关的不确定性。此外，增强的 P-A 图可以同时解释预测准确性和不确定性，使其成为模型评估的潜在工具。根据结果​​，确定了8个高回报、低风险的潜在区域作为优先勘探区域。该研究不仅为矿产远景图绘制和不确定性评价提供了新方法，也为该地区的矿产勘查提供了指导。