The code for the Bagging-based PU learning with Bayesian hyper-parameter optimization for 3D mineral potential mapping;
The random forest and OCSVM were provided by scikit-learn 0.24.0;
The continuous weighting approach please refer to Yousefi, M., Carranza, E.J.M., 2015a. Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping. Comput. Geosci. 74, 97–109;
The WofE please refer to Li, R., Wang, G., Carranza, E.J.M., 2016. GeoCube: A 3D mineral resources quantitative prediction and assessment system. Comput. Geosci. 89, 161–173.
If you use the Bagging-based PU learning with Bayesian hyper-parameter optimization, Please cite this article as: Zhang, Z., Wang, G., Liu, C., Cheng, L., Sha, D., Bagging-based positive-unlabeled learning algorithm with Bayesian hyperparameter optimization for three-dimensional mineral potential mapping, Computers and Geosciences (2021), doi: https://doi.org/10.1016/j.cageo.2021.104817.