Using the same features we use GBR to model the Poisson's ratio for the dataset used in the Anomaly detection method. We used 4-fold validation to train and test on the total dataset, the results are shown in Figure b. Including the auxetic materials deteriorates the performance of the model, especially in predicting the auxetic materials itself. This further justifies the treatment of auxetic materials as anomalies. Additionally, this plot proves that the features are optimal for learning Poisson's ratio pattern for non-auxetic materials. Therefore a similar feature set was used for training the autoencoders for anomaly detection.