A Simple Guide to Machine Learning Visualisations

The Yellowbrick library also contains a set of visualisation tools for analysing clustering algorithms. A common way to evaluate the performance of clustering models is with an intercluster distance map. The intercluster distance map plots an embedding of each cluster centre and visualises both the distance between the clusters and the relative size of each cluster based on membership. We can turn the diabetes dataset into a clustering problem by only using the features (X). Before we cluster the data we can use the popular elbow method to find the optimal number of clusters.