Challenges and considerations when dealing with imbalanced data
In certain instances, directly using data for machine learning without worrying about data imbalance can yield usable results suitable for a given business scenario. Yet, there are situations where a more dedicated effort is needed to manage the effects of imbalanced data.
Broad statements claiming that you must always or never adjust for imbalanced classes tend to be misleading. The truth is that the need to address class imbalance is contingent on the specific characteristics of the data, the problem at hand, and the definition of an acceptable solution. Therefore, the approach to dealing with class imbalance should be tailored according to these factors.
