Hands-On Data Science and Python Machine Learning

Within set sum of squared errors (WSSSE)

Now, how do we measure how good our clusters are? Well, one metric for that is called the Within Set Sum of Squared Errors, wow, that sounds fancy! It's such a big term that we need an abbreviation for it, WSSSE. All it is, we look at the distance from each point to its centroid, the final centroid in each cluster, take the square of that error and sum it up for the entire Dataset. It's just a measure of how far apart each point is from its centroid. Obviously, if there's a lot of error in our model then they will tend to be far apart from the centroids that might apply, so for that we need a higher value of K, for example. We can go ahead and compute that value and print it out with the following ...