Understanding XG Boost Training (Multi class classification) I have been working with XG boost for classification (multi class classification : 6 classes)
I use 5 fold CV to train and validate my model.

Please refer to the paramters, which i had used in my model.

 params    = {"objective": 'multi:softprob', "eta": 0.1, "max_depth": 7,
               "min_child_weight": 4,"silent": 1, "subsample": 0.8,
               "colsample_bytree": 0.8, "num_class" : 6, "gamma" : 0, 
               "eval_metric" : 'merror', "seed": 0}


I plotted the training and testing error for each fold in a 5 fold CV.


Questions:


What can I understand/interpret from the training &amp; test loss graph ? 
Training error reduces to zero, but the testing error reduces over a period &amp; become idle.


I am not sure whether the model is 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 ?
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.