A quick curosry glance at the central diagonal tells me that Finance, Personal/Programming, Professional/EPFL, and Group work are the categories of e-mail which are most likely to be categorized incorrectly by the software. Looking at the columns tells me that the Academic and Personal are going to have the most misfiled messages in them.
Yes, but how likely is each mislabelling? There is no scale to indicate how the colors map onto probabilities.
As well as providing a scale, it would be helpful to make the heatmap an annotated heatmap, in which each square is labelled with the corresponding value (perhaps with values below some threshold omitted to reduce clutter).
Example: https://web.stanford.edu/~mwaskom/software/seaborn/_images/s...
(from the Seaborn docs: https://web.stanford.edu/~mwaskom/software/seaborn/generated...)
Edit: Consider another question - If an email has the true label 'Financial', is it more likely to be mislabeled or correctly labelled? I can guess, but without more knowledge of the scale I can't be certain.
As it is, the specifics of how it mislabels his e-mail is not all that interesting to me, as I don't have access to his e-mail and so the specific numbers are pretty much irrelevant.
How on earth am I to tell from that visualization how often it mislabels financial emails as personal? Eyedropper the colors and hope the values in the shades of blue follow a linear scale?