Scikit-Learn One-hot-encode before or after train/test split

Question

I am looking at two scenarios building a model using scikit-learn and I can not figure out why one of them is returning a result that is so fundamentally different than the

Answer 1

I can't get your code to run, but my guess is that in the test dataset either

• you're not seeing all the levels of some of the categorical variables, and hence if you calculate your dummy variables just on this data, you'll actually have different columns.
• Otherwise, maybe you have the same columns but they're in a different order?