Very interesting. Thank you. However, I’ve found that with facial recognition, using the value found with NearestNeighbors (0.5–0.6) leads to many different faces being clustered under the same label_. By analysing the dataset manually, and with some try and error, I’ve found that the best epsilon value for facial encodings is between 0.3 and 0.4. How would you explain this?