When should we discretize/bin independent variables/features and when should not? My attempts to answer the question: - In general, we should not bin, because binning will lose information. - Binning is actually increasing the degree of freedom of the model, so, it is possible to cause over-fitting after binning. If we have a "high bias" model, binning may not be bad, but if we have a "high variance" model, we should avoid binning. - It depends on what model we are using. If it is a linear mode, and data has a lot of "outliers" binning probability is better. If we have a tree model, then, outlier and binning will make too much difference. Am I right? and what else? ---------- I thought this question should be asked many times but I cannot find it in CV only these posts http://stats.stackexchange.com/questions/153400/should-we-bin-continuous-variables http://stats.stackexchange.com/questions/68834/what-is-the-benefit-of-breaking-up-a-continuous-predictor-variable