title: Imbalanced multiclass classification dataset: undersample or oversample?
body: <p>Dataset has around 150k records with four labels: ['A','B','C','D'] and the distribution is as follows: <br>
  A: 60000 <br>
  B: 50000 <br>
  C: 36000 <br>
  D: 4000 <br></p>
  <p>I notice using the package classification report to get the precision, recall, and f1-score, the f1-score is causing an UndefinedMetricWarning because class D is not being predicted due to the low number of records.</p>
  <p>I know that I need to perform oversample/undersample to fix the imbalanced data.</p>
  <p>Question: Would it be a good idea to fix the imbalanced data but randomly sample 4000 records from each class so that it is balanced?</p>