In this work, both full features without the feature selection and feature subset with the feature selection are attempted to examine whether the feature selection is beneficial to our bitterrant/non-bitterant classification. Herein, feature selection is conducted based on the feature importance (Teixeira et al., 2013) derived from the model-training with the random forest (RF) method, which will be elaborated in the following section. Thus, the full features without the feature selection, and the feature subset after the feature selection are adopted as the molecular descriptors to systematically evaluate the performance of bitter/bitterless classification.