title: How to improve performance in multi-class?
body: <p>I'm classifying traffic using the CIC-2020 Darknet data set.
  There are 8 classes in the data set.
  I tried a lot of different ways.  (Over sampling, Under Sampling, Complex Sampling and Outlier Elimination, Feature Selection, model change - cat boost, rus boost, Decision Tree, Random Forest, OvO, OvR, SVM etc..)
  However, the performance doesn't improve.
  In the picture below, I only selected features using RFECV and used Light GBM.  This has the best performance.</p>
  <p><a href="https://i.stack.imgur.com/jeodr.png" rel="nofollow noreferrer">LightGBM Performance</a></p>
  <p>Is there a good way to improve performance in multi-class classification?
  Please help me.</p>