I have some continuous variable in my data that I wish to apply binning for. The values range from 0 to 800 but I got motivated by the fact that the data distribution was left skewed as you could see in the following figure:
However, I have read this amazing blog about binning data this whereby the author claims that
adaptive binning is better than
fixed-width binning. I understand the idea behind this because the some of the bins that we define in the fixed-width approach may have too little data distribution in comparison to other bins, which won't be a fair game to play whereby the adaptive approach, motivated by the idea of
quantiles is better. Are there any more arguments, or more in depth analysis, of this hypothesis ?