According to the histogram in Fig. , the most frequent label is "fruity", while the least frequent is "fennel/cedar", This indicates that the dataset has a notable class imbalance. Instead of using random sampling, we used iterative stratified sampling to guarantee a balanced representation of the data in both the training and test sets. This approach, implemented using the 'iterative_train_test_split' function from the scikit-multilearn library , helps to prevent the unintentional exclusion of minority odor descriptors that can occur with random sampling, which may worsen label imbalance. Figures