Visualise a Confusion Matrix Using Sankey Diagram

A confusion matrix is mostly viewed either as a raw NumPy array or a visualization, as depicted below:

I find both of them difficult to interpret.

• The NumPy array does not tell whether rows depict the predicted label or the true label (the same thing is observed with columns).

• The plot on the right depicts what’s missing from the NumPy array, but these raw prediction counts in each cell force me to do some mental calculations to determine precision and recall.

If you face similar issues, try plotting a confusion matrix as an interactive Sankey diagram.

This is demonstrated below:

Here’s how it solves the problems discussed earlier:

• Firstly, as shown above, one can interactively determine the number of instances belonging to each class and how they were classified.

• Next, hovering over the connections gives more info about those instances, which can offer better interpretability, for instance:

  • “10 Fraud instances correctly classified as Fraud.”

  • “2 Legit instances incorrectly classified as Fraud.”

  • and more.

• The Sankey diagram also helps me get a visual estimate for precision and recall:

Of course, everyone has visualization preferences, so do share your opinion about this.

Is this a better approach over the traditional ones?

That said, if you face issues with interpreting the confusion matrix, here’s a video I published in this newsletter once:

We also covered Sankey diagrams pretty recently in the newsletter, which you can find below:

You can find the code for creating the above confusion matrix using a Sankey diagram here: Jupyter Notebook.

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