<s>[INST] Let's say there is a political blog with thousands of posts, written by various authors, and we are trying to predict the party affiliation of an author solely based on the text of their posts. 

This might be challenging because you might have posts from political independents and third party members. However, these can be filtered later. 

Assuming we have a clean and labeled dataset, and if we use supervised learning. We assume that the labels are more or less reliable. 

Given the context, how should one handle imbalanced data (where one class has significant more instances than others) when training the model?[/INST]