October 2017
Beginner to intermediate
270 pages
7h
English
Content preview from Machine Learning for Developers
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The Elbow method
One of the questions that may have arisen when implementing K-means could have been "how do I know that the target number of clusters is the best or most representative for the dataset?"
For this task, we have the Elbow method. It consists of a unique statistical measure of the total group dispersion in a grouping. It works by repeating the K-means procedure, using an increasing number of initial clusters, and calculating the total intra-cluster internal distance for all the groups.
Normally, the method will start with a very high value (except if we start with the right number of centroids), and then we will observe that the total intra-cluster distance drops quite quickly, until we reach a point where it doesn't change ...
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