XGboost - Choice made by model i am using XGboost to predict a 2 classes target variable on insurance claims. I have a model ( training with cross validation, hyper parameters tuning etc...) i run on another dataset. 

My question is :

is there a way to know why a given claim has been affected to one class i.e. the features that explain the choice made by the model?

The purpose is to be able to justify the choice made by the machine to third party human.

Thanks for your answer.
You can use the ELI5 library to explain the feature contributions to individual predictions for XGBoost models.

See Explaining Predictions in the docs, copied below:


  To get a better idea of how our classifier works, let’s examine
  individual predictions with eli5.show_prediction():

from eli5 import show_prediction
show_prediction(clf, valid_xs[1], vec=vec, show_feature_values=True)