When should we discretize/binning independent variables / features and when 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?

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- I thought this question should be asked many times but I cannot find it in CV only this one: http://stats.stackexchange.com/questions/153400/should-we-bin-continuous-variables. The answer is really short. I wish we have a long answer or formal references.
- Feel free to mark this question as too general or duplicate. I am still learning how to ask..