Is it bad practice to arbitrarily change ordinal categorical variables to numeric (and vice versa) to improve model performance?

Or is it okay to do so? Data analyst here, still very newb at data science. I found that switching a feature between categorical and numeric (where either option make sense) had different impacts on model accuracy.

Can I make some features categorical and some numeric? Or should you stick to the same rule for the entire dataset?

change datascience ordinal performance