Improvements to Silhouette Visualizer

The following improvements to the Silhouette Visualizer are left over from #91:

Note to contributors: items in the below checklist don’t need to be completed in a single PR; if you see one that catches your eye, feel to pick it off the list!

• Improve the documentation describing what Silhouette scores are and how to use the visualizer to qualitatively evaluate a clustering solution.
• Find a real world example rather than just using make_blobs (note: we also have an example using the Iris dataset; ideally we’d having something a bit more unique to YB that we can add to yellowbrick.datasets module - perhaps this should be a separate issue?).
• Instead of hard fixing the limits of the X-axis from -1.0 to 1.0; be more flexible so that the visualizer has a better display (or give the user the option of setting the limits).
• Move the cluster identity labels away from the middle and to the y-axis.
• Add ability to define cluster colors and improve color selection methodology.
• Add a legend/annotation that describes the average clustering coefficient (e.g. label the red axvline)

[mgarod]

Bullet point Add a legend/annotation that describes the average clustering coefficient (e.g. label the red axvline) has been addressed with #839 merged

[bbengfort]

@gokriznastic glad to hear you’re willing to keep going with the updates to this visualizer. The label should indicate the value of the SilhouetteVisualizer.silhouette_score_ property, which is what is plotted by the red axvline: L178. This may be as simple as just adding a label="" keyword argument to the axvline; it’ll just be a matter of how we communicate this score. Note that if you need to add math to the matplotlib figure, you can format the label with $ as in $S_i=0.2$ will render the latex math. Not sure if this is necessary or not, but perhaps there is a symbol for the mean silhouette score.

As for tick box 2 - I appreciate the Iris data set, but I was hoping for something a bit more unique to us.

Also, let’s not forget the user specified limits to the figure!

Thanks again!