The process employed to discover new materials for specific applications typically utilizes screening of large compound libraries. In this approach, the performance of a compound is correlated to the properties of elements referred to as descriptors. In the effort described below, we developed a simple and efficient machine learning (ML) model for predicting adsorption energies of CH4 related species, namely, CH3, CH2, CH, C, and H on the Cu-based alloys. The developed ML model predicted the DFT-calculated adsorption energies with 12 descriptors, which are readily available values for the selected elements. The predictive accuracy of four regression methods (ordinary linear regression by least-squares (OLR), random forest regression (RFR), gradient boosting regression (GBR), and extra tree regression (ETR)) with different numbers of descriptors and different test-set/training-set ratios was quantitatively evaluated using statistical cross validations. Among four types of regression methods, we have found ...