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I have a regression model where my target variable (days) quantitative values ranges between 2 to 30. My RMSE is 2.5 and all the other X variables(nominal) are categorical and hence I have dummy encoded them. I want to know what would be a good value of RMSE? I want to get something within 1-1.5 or even lesser but I am unaware what I should do to achieve the same.

Note# I have already tried feature selection and removing features will less importance.

Any ideas would be appreciated.

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If your x values are categorical then it does not necessarily make much sense binding them to a uniform grid. Who's to say category A and B should be spaced apart the same as B and C. Assuming that they are will only lead to incorrect representation of your results.

As your choice of scale is the unknowns, you would be better in terms of visualisation to set your uniform x grid as being the day number and then seeing where the categories would place on the y scale if given a linear relationship.

RMS Error doesn't come into it at all if you don't have quantitative data for x and y.

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I am not sure if my question wasnt clear enough. My Y or targer variable is quantitative but my X variables are categorical. I have already done Analysis of Variance and selected the ones which show higher variance.
Yes but a linear regression is an attempt to fit two quantitative variables to a linear equation. As your x variable is not quantitative, a linear regression is not appropriate unless you can map these categories into a quantity.
I have dummy encoded them as i said before ... so they are vectors of 0's and 1's now...
Don't dummy encode them. Encode them based on a value that will give you a linear relationship with zero error.

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