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All of these points are not fundamental problems. Researchers are not writing expert driven rules where the "raw" accuracy of structured data is that important.

All of these points are not fundamental problems. Researchers are not writing expert driven rules where the "raw" accuracy of structured data is that important. The only important part is making sure that the training labels are accurate. If the training data has inaccuracies, the model should learn to work around them (with some accuracy cost). The main question simply is how much signal is in the data. And that's hard to predict in advance (see the recent surprising results about predicting sexual orientation from faces).

YeGoblynQueenne 9y ago
>> And that's hard to predict in advance (see the recent surprising results about predicting sexual orientation from faces). Those results were not so much surprising, as coerced. For example, training data included an equal amount of men of both sexual orientations and the same for women- an equal distribution, in stark contrast to the one in the real world. This was clearly done to overcome the inevitably poor accuracy when training with unbalanced classes. In short: don't base anything on that paper, for it was a steaming pile.