How to choose an optimal threshold for binary discretization We know that we usually do discretizations to continuous features to remove extra information and unwanted regularities, which makes the model robust and well-predicted.
But I am wondering except based on the context of feature how to choose an optimal threshold for data discretization, binary or multi.
Inspecting the histogram chart of that feature by different numbers of bins can be helpful. the maximum width of the bins in which the distribution of the data does not change noticeably can be a suitable choice.