title: sklearn k means cluster labels vs ground truth labels
body: <p>I'm trying to learn sklearn. As I understand from step 5 of the following <a href="https://www.datasciencelearner.com/k-means-clustering-in-python-label-dataset/" rel="nofollow noreferrer">example</a>, the predicted clusters can be mislabelled and it would be up to me to relabel them properly. This is also done in an <a href="https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_iris.html" rel="nofollow noreferrer">example on sci-kit</a>. Labels must be re-assigned so that the results of the clustering and the ground truth match by color. </p>
  
  <p>How would I know if the labels of the predicted clusters match the initial data labels and how to readjust the indices of the labels to properly match the two sets?</p>