Let us step back and think again what we are trying to achieve here. Actually, we do not need a classifier that perfectly predicts good and bad answers, as we measured it until now using accuracy. If we can tune the classifier to be particularly good in predicting one class, we could adapt the feedback to the user accordingly. If we had a classifier, for example, that was always right when it predicted an answer to be bad, we would give no feedback until the classifier detected the answer to be bad. Contrariwise, if the classifier succeeded in predicting answers to be always good, we could show helpful comments to the user at the beginning and remove them when the classifier said that the answer is a good one.
To find out which situation we are in here, we have to understand how to measure precision and recall. To understand this, we have to look into the four distinct classification results as they are described in the following table:
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Classified... | |||
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