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The only case where I would consider resampling data is when there is a requirement to improve recall for a particular class. Thus the goal would be to force the classifier to predict this class more often, even though it usually means decreasing performance in general. Resampling is an easy method but rarely the optimal one. In general I'd first do an ...


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Two-stage / hierarchical classification models are very useful. Typically, the first stage is binary. It predicts the presence or absence of a low-rate event. If present, the second stage categories the type of event. It is easier to train the models and typically the predictive ability of the models is higher.


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You have $98\%$ in one class, right? This means that, knowing nothing about the data, you should be able to get $98\%$ of them right by guessing that majority class. If you get $97\%$ of them right, that sounds like an $\text{A}$ in school and thus a good model, but the model does worse than randomly guessing! Better yet, compare using proper scoring rules ...


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What is the purpose of the analysis? What are the criteria of primary interest (accuracy)? The class imbalance problem stems from having insufficient data from the minority class to adequately characterise it's distribution. This means imbalance is only a problem if you have a small dataset, if you have lots of data, the imbalance problem generally ...


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