Understanding XG Boost Training (Multi class classification)
What can I understand/interpret from the training &amp; test loss graph?


This checking out the quality of the model. If the train and test set loss decreasing according to the number of epoch in the same way(i.e plots should overlap each other), that means that model is good. Otherwise, we have a problem. In your graph plots fastly separates(from x-value moreover 10 or 20). In this case we could say that's overfitting.


  I am not sure whether the model is overfitting ?


It's typical overfitting.


  How can I reduces the error b/w training &amp; testing, only through hyperparam tunning or this is how the XG Boost model works ?



More data.
Feature selection.
Cross-validation.
Wide range hyperparameters to explore.
Random search.