In this example, we continue using the MNIST dataset (the X_train array is the same defined in the paragraph dedicated to KNN), but we want also to analyze different clustering evaluation methods. The first step is visualizing the inertia corresponding to different numbers of clusters. We are going to use the KMeans class, which accepts the n_clusters parameter and employs the K-means++ initialization as the default method (as explained in the previous section, in order to find the best initial configuration, Scikit-Learn performs several attempts and selects the configuration with the lowest inertia; it's possible to change the number of attempts through the n_iter parameter):
import numpy as npfrom sklearn.cluster ...
