C.2 Study of canonicalization.

As mentioned in the main text (Table 7), canonicalizing raw machine learning experience into natural
language is essential to unleashing the power of language models. The complete ablation results of
canonicalization are shown in Table 14. Overall, discretization is necessary for good performance in
MLCopilot because sending numbers directly is not effective, and the split points should be based on
the statistics of the best solutions to avoid landing on insensitive points.

24

Prompt

Space description

Demonstrations

Here are some classiﬁcation datasets along with best hyper-parameter conﬁgurations to train a R
language model "Learner mlr.classif.svm from package(s) e1071" on them.
MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks