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A classification system I built is going to go into production soon (it'll be part of a larger dashboard), and I'm looking for ways to better visualize and convey to business folks the results of a classification.

Basically, given "old" data on which the model was trained, I predict the classes of "new" data, with the goal being to show whether the class distributions for the new data are statistically indifferent from the class distributions observed in the old data. So, if there are three classes A, B, and C in the old data, with proportions of 50%, 30%, and 20%, respectively, I compare the classification distribution for the new data with those original, observed proportions.

Outside of a confusion matrix (which I think is probably inappropriate for most dashboard users), how else can I effectively present these results? I was thinking of a bar chart like this:

enter image description here

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    $\begingroup$ The suggestion provided by @scherm is fine if your goal is just to present the overall percentages of class predictions, but to be clear, it does not reflect the accuracy of the model. Apologies if this is out of the scope of your original question, but want to reinforce that even if your new data (predictions) has distributions 50%, 30%, 20% for classes A, B, C, it does not prove that the model is classifying properly. $\endgroup$ – HEITZ Nov 22 '16 at 23:50
  • $\begingroup$ A significant deviation from old to new could provide evidence for the new data not fitting well into the previous classes (i.e. that the predictions went horribly awry somewhere in the process). This is a problem I'm grappling with constantly, but for now I'm just trying to visualize some of these results. $\endgroup$ – blacksite Nov 23 '16 at 1:13
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Here are a couple of options. I prefer the stacked column chart, which could also be annotated with the actual proportions if needed.

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