Increase accuracy of classification problem
I'm not familiar with scikit but I'm assuming that TfidfVectorizer represents bag of words features right? By this I mean that it treats all the instructions in an instance as a set, i.e. doesn't take into account their sequential order.

I'm also not familiar with compilers but I'm guessing that the order of the instructions could be a relevant indication? I.e. a compiler may generate particular sequences of instructions.

Based on these remarks I would try to represent instances with n-grams of instructions rather than individual instructions. Then you can still use some kind of bag-of-ngrams representation, possibly with TFIDF, but I would start with simple binary or frequency features. A simple feature selection step with something like information gain might be useful.

[edit] N-grams take order into account locally. In a bag of words model, words (or instructions in your case) are considered individually of each other: for instance the sequence push, push, mov is the same as push, mov, push. With bigrams this sequence would be represented as (push,push), (push,mov) whereas the second one is (push,mov), (mov,push). This means two things:


Higher level of detail about the instance, which can help the model capture the relevant indications
More features so higher risk of overfitting (the model taking some random details as indication, which lead to errors on the test set).