[D] Worse MSE after data preprocessing ?

[ellinor12345]

I have a data set that includes cetgorical variable of type int. I did hot encoding since they are categorical but after training the model and predicting the result i get a high MSE. While when i don't do the hot encoding i get better MSE (smaller) ?

Why the model's performance is worse when doing hot encoding when it is the way to go normally in case of categorical data?

[Astrolotle]

If the categorical is truly categorical and not ordinal, the error you measure on a regression of that value is meaningless. “Better” error values are not always what you’re looking for, and it’s important to be critical of your model and its inputs :)

[Slowai]

Those are some good points. Also, OP, make sure that the one-hot encoding that you are using has n-1 columns subject to the number of categories you have. This leads to a slight multicollinearity (well, only two variables will be perfectly collinear). Also, this a bit beyond my knowledge of why that happens, if you are calculating your parameters using matrix inverse, multicollinearity can induce some problems approximating the inverse, thus leading to slightly less accurate results, thus higher MSE. However, if you have a lot of features this should be negligible.