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It would be much better if journalists would stop reporting "accuracy" and instead mention the precision and recall[1] of the classifiers they report on. There'

It would be much better if journalists would stop reporting "accuracy" and instead mention the precision and recall[1] of the classifiers they report on. There's a fundamental trade off between the two and it's hard to tell what where the trade off has been made unless both are reported. To elaborate, it's trivial to make a classifier with 0 false positives... but also many false negatives (high precision, low recall). Similarly, we can have 0 false negatives but many false positives (low precision, high recall). [1] - https://en.wikipedia.org/wiki/Precision_and_recall https://en.wikipedia.org/wiki/Precision_and_recall

yorwba 8y ago
This isn't a binary classifier, they're trying to determine the user who posted a given set of tweets among a certain set of Twitter users known to contain that user. The terms "positive" and "negative" don't apply to that kind of non-binary problem, and neither do precision and recall. Accuracy is a completely appropriate measure of performance in this case. Precision and recall can be useful for multi-class classification problems, but only if there's a "background" class for negative results. E.g. if you're evaluating a cancer detector, then you might care about it's precision when detecting any cancer vs. no cancer, but then the classifier has been reduced to a binary decision. It's also sometimes useful to focus on a certain class (e.g. skin cancer) to see whether the classifier has difficulties with that specific one, but again that's treating the multi-class classifier as a binary classifier.