Hi, so appreciated your suggestions, I read the letter and checked through my operations. Unfortunately, the problem still exists. I guess providing more details could be convenient for you to locate the problem. So I post my code and test data, and they are all in test.zip. Now, let me describe my processing flow： 1. In test.zip, I constructed some data for test, they all categorical features, fifteen columns in total. I saved data as test/test.csv . 2. For some reasons, I need to cross the conda environments. At first, I use a conda environment, which includes python 3.10, sci-kit learn 1.2.1, and does not include hummingbird-ml. I construct an OneHotEncoder of sklearn, and then fit the test data. Finally, I save the encoder/pipeline as a binary file by pickle. You could find the code in test/A.py . 3. Then, I use another conda environment, which includes python 3.8, sci-kit learn 1.2.1, and hummingbird-ml 0.4.7. I load my sklearn preprocessor from the binary file by pickle, and then use hummingbird-ml to covert it. Finally, I check the outputs from sklearn and hummingbrid-ml, however, the shapes are different. You could find the code in test/B.py. 4. I found that if I modify the code on line 16 of test/A.py. From OneHotEncoder(sparse_output=False, handle_unknown="infrequent_if_exist", min_frequency=0.005) to OneHotEncoder(sparse_output=False, handle_unknown="ignore"), then everything is ok. I found the changelog of sklearn, it said since version 1.1, sklearn provides the new choice of handle_unknown, which I would like to use but caused the problem. Could you look into my operations and codes? Did I make a mistake in any step? Or is there a solution to fix the problem? I appreciate your reading and efforts. Thanks again for all your work in hummingbird-ml. It's an awesome project, and I hope I could use it all the time. Yours sincerely, faterazer

So glad my example helped. I really hope that the problem could be solved in the near future. Thanks your efforts. 🙂