Тип публикации: статья из журнала
Год издания: 2024
Идентификатор DOI: 10.1051/e3sconf/202458301007
Аннотация: <jats:p>This work is devoted to the classification of rock types based on their geochemical characteristics using machine learning methods. The study used data on the content of various elements in rocks to develop classification models. Four methods were investigated and compared: decision tree, logistic regression, random forest Показать полностьюand gradient boosting. The results showed that the random forest model demonstrates the highest classification accuracy (0.832612), which is explained by its ability to efficiently process a variety of features and their interactions. Correlation analysis has shown significant correlations between the geochemical characteristics of rocks, which underlines the importance of choosing appropriate machine learning methods for processing such data. This work highlights the importance of using ensemble methods that can take into account complex interactions between features for accurate classification of geochemical data and can be useful for specialists in the field of geology, mining and related industries.</jats:p>
Журнал: E3S Web of Conferences
Выпуск журнала: Т. 583
Номера страниц: 01007
ISSN журнала: 25550403
Место издания: Les Ulis
Издатель: EDP Sciences - Web of Conferences