What the model actually used for classification were these: ‘posting’, ‘host’, ‘NNTP’, ‘EDU’, ‘have’, ‘there’. These are meaningless artifacts that appear in both the training and test sets and have nothing to do with the topic except that, for example, the word “posting” (part of the email header) appears in 21.6% of the examples in the training set but only two times in the class “Christianity.”
Is this model going to generalize? Absolutely not.
An Example from Image Processing
In this example using Google’s Inception NN on arbitrary images the objective was to correctly classify “tree frogs”. The classifier was correct in about 54% of cases but also interpreted the image as a pool table (7%) and a balloon (5%).
Looks like an interesting paper. Click through for a link to the paper.