comparing these images to the random sample of face images from figure - we can guess why they were labeled as noisethe fifth image in the first row shows person drinking from glassthere are images of people wearing hatsand in the last image there' hand in front of the person' face the other images contain odd angles or crops that are too close or too wide this kind of analysis--trying to find "the odd one out"--is called outlier detection if this was real applicationwe might try to do better job of cropping imagesto get more homogeneous data there is little we can do about people in photos sometimes wearing hatsdrinkingor holding something in front of their facesbut it' good to know that these are issues in the data that any algorithm we might apply needs to handle if we want to find more interesting clusters than just one large onewe need to set eps smallersomewhere between and (the defaultlet' have look at what different values of eps result inin[ ]for eps in [ ]print("\neps={}format(eps)dbscan dbscan(eps=epsmin_samples= labels dbscan fit_predict(x_pcaprint("clusters present{}format(np unique(labels))print("cluster sizes{}format(np bincount(labels ))out[ ]eps= clusters present[- cluster sizes[ eps= clusters present[- cluster sizes[ clustering