@bartolsthoorn
Accuracy is probably not what you want for Multi-Label classification especially if your classes are unbalanced.
Let’s say you have a class A present for 90% of your dataset, and classes B and C that occurs about 10% of the time, a model that always return class A and never class B and C will have 70% accuracy but no predictive power.
Your metric should be taking into account False positive and False negative. You can check Matthews correlation coefficient or Fbeta score (often F1 score) or Hamming loss that are designed for Multilabel classification and are implemented in Scikit-learn.
Also make sure to read on Precision and Recall.