There are two types of predictive models for polar-izability with lower computational burden than electronic structure calculations. Firstly, approximations for polarizability can be constructed based on the group contribution principle, which divides polarizability into atomic or bond contributions . These models can offer somewhat accurate predictions with minimal molecular information and computational effort, and we will assess such models in this work. As a second approach, machine learning (ML) models have been proposed as a cost-effective solution with improved accuracy . However, the training process and accuracy of the ML models are strongly dependent on the features of the dataset (e.g., chemical diversity, molecular size, number of samples) as well as on the ML method itself.