Dear All, I have a *screening* lab test and I am trying to minimize the False negative value in the recall (TP/(TP+FN)) therefore I want to increase the cost whenever an FN is found in the training. I understand that in R they have some kind of loss matrix that penalize the FN during fitting. my Postive classes percentage is 30 % On the forums and StackOverflow, they suggest using class_weight=balanced in the decision tree which oversamples the class with the lowest frequency. However, I don't see how that helps in minimizing the FN.
Any suggestions? Bests Nadim -- Nadim Farhat
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