Regularization in machine learning models for MVT Pb-Zn prospectivity mapping: applying lasso and elastic-net algorithms

• Published in: Earth science informatics Vol. 17; no. 5; pp. 4859 - 4873
• Main Authors: Hajihosseinlou, Mahsa, Maghsoudi, Abbas, Ghezelbash, Reza
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2024; Springer Nature B.V
• Subjects: Algorithms; Datasets; Earth and Environmental Science; Earth Sciences; Earth System Sciences; Elastic limit; Information Systems Applications (incl.Internet); Machine learning; Ontology; Performance evaluation; Regularization; Simulation and Modeling; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Tectonics; Elatic-net; Random search algorithm; Mineral prospectivity mapping; Lasso; MVT Pb-Zn
• ISSN: 1865-0473; 1865-0481
• DOI: 10.1007/s12145-024-01404-5

The current research employed the least absolute shrinkage and selection operator (Lasso) and Elastic-net algorithms to examine their potential utilization in MVT Pb-Zn prospectivity modeling. In training the model, both Elastic-net and Lasso regularization approaches include a penalty term to the loss function. Since this penalty term limits the feature coefficients, the model is motivated to prioritize the most informative features and penalize the less relevant ones. The Varcheh district in western Iran was the source of the geological, geochemical, tectonic, and alteration dataset. We applied stratified 5-fold cross-validation to train the dataset, ensuring consistent and comprehensive performance evaluation across different data subsets. This method improved data utilization and provided more reliable performance estimates by averaging metrics over multiple folds, thereby enhancing the model’s generalization assessment. The hyperparameters were adjusted using random search, quickly finding near-optimal solutions. Our investigation revealed that Elastic-net exhibited superior prediction accuracy and model robustness compared to Lasso. The combination of L1 and L2 regularization in Elastic-net, offers a more adaptable technique than Lasso, which just utilizes L1 regularization. This feature enables Elastic-net to handle scenarios in which there have been correlated predictors successfully.