Let's return to our earlier discussion on what is the right value for k. In the preceding example, it is more intuitive to set it to 3 since we know there are three classes in total. However, in most cases, we don't know how many groups are sufficient or efficient, while the algorithm needs a specific value of k to start with. So, how can we choose the value for k? There is a famous approach called the Elbow method.
In the Elbow method, different values of k are chosen and corresponding models are trained; for each trained model, the sum of squared errors, or SSE (also called the sum of within-cluster distances) to centroids is calculated and is plotted against k. Note, for one cluster, the squared error (or the within-cluster ...
