Hi, I wanted to ask if there was some built-in way to perform a pairwise classification similar to how one would do correlations with one_minus_correlation. Suppose I give my classifier 4 targets. I want the classifier to take all possible pairs including target pairs like (1,1) (the latter is in order to see how noisy my data is). That makes 4*3+4 pairs or at least 4*3/2+4 due to confusion matrix being symmetric in this case. Then we should calculate classification accuracy for each pair, i.e. how many times each of the two targets were correctly predicted.
Obviously, I could do that in a for-loop but I was wondering if there was some cleaner (and faster) way. As far as I understand, by default classifiers report a multiclass classification performance? Is that a better approach than doing forcing binary classification on multiclass data? Thanks! Jonas
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