Is it a good practice to remove observations with very less frequency from the data?




Suppose while exploring some data, I see the histogram of a variable like this one

Then is it a good and helpful practice to assign to the observations with very low frequencies the values with higher frequencies or even removing those observations? Does this help in improving models?




The answer would depend on the case, where how much information would this particular information capture.

Even if the volume is low, you can have a pocket of very high signal, which can be a micro segment in itself. So, while removing less frequency observations can be a good option, it is definitely not true always.

Hope this helps.