The bagging approach was applied to the full testing dataset. This approach involves the segmentation of the large dataset into bags of smaller datasets for predictions. The dataset, comprising 3445 MOFs and over 5 million simulation data points obtained through GCMC simulations, was systematically organized into 100 bags. Each bag was designed to contain diverse data samples, systematically categorized by fugacities and adsorbates. This categorization also ensured variability and comprehensive coverage within each set while incorporating all 3445 MOFs (represented by their PCs) in every bag. The purpose of the bagging process simply to parallelize the testing of the model.