Why is XGBClassifier in Python outputting different feature importance values with the same data across different repetitions?
Look at the 'max_features' parameter in the GradientBoosting Classifier in scikit learn, here: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier
That means that the algorithm is fitting different trees to try and account for the residual information after each sequence of trees, and that the features for each tree can be randomly permuted based on this parameter's selection.
Scikit learn does say that this can be made deterministic by setting random_state...