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If we build a classifier based on a very small number of instances (say, fewer than 300) and the number of features we are using is very large (say, larger than 100k features). If we decide to introduce a feature selection step before building the classifier, is there a rule of thumb on how many features we should choose?

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  • $\begingroup$ I would think this problem is too data specific for there to be any rule of thumb. $\endgroup$
    – Glen
    Mar 31, 2012 at 3:13

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It depends on the type of your target variable. Frank Harrell gives some guidelines in his book, "Regression Modeling Strategies". You can find the discussion here (page 61): Regression Modeling Strategies

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