Python sklearn model.predict() gives me different results depending on the amount of data
If XGBoostClassifier is fed the same input data over and over again it will yield the same results. There is no inherent randomness in this classifier that would different results for the same input. Additionally - there should be no difference in the result of an individual prediction if it's requested in a smaller batch versus a larger batch (again the result will be identical).
On the other hand - if you train XGBoost on different data their outputs will definitely be different. If you add new data to the underlying dataset &amp; train with that - new &amp; different patterns will emerge that XGBoost will try to take advantage of and the entire tree network will be fit very differently.
I suspect what you are observing is a bug in structuring your input data that you are then feeding to the .predict() method. If you share a sample of your code maybe we can drill-down on the issue.