What is a good classification accuracy in data mining?
What a good question! Or what a bad question should I say. In fact, this question is not a good one since if we ask it this way, we might expect an answer that is valid for any data mining problem. This is of course not possible. This question may be asked by a data miner, since it’s one way of measuring the quality of the data mining algorithm. Indeed, you can estimate how good your decision tree or neural networks are by estimating the classification rate of the test set. My point in this article is to highlight the fact that the classification percentage depends on the application in which data mining is used. Let me explain that with a few examples from my own experience. I have a friend working in the domain of face recognition. According to him, an algorithm (machine learning in his case) is well fitted to the problem when you get a classification accuracy above 97% for example. This may be true, but only in his domain, which is face recognition. In this domain, you apply machine
