What is the difference between RandomForestClassifier and XGBRFClassifier?

There is no detailed explanation about what XGBRFClassifier exactly is so I was wondering.

  • $\begingroup$ The following sentence from the xgboost documentation should answer your question: "XGBRFClassifier and XGBRFRegressor are SKL-like classes that provide random forest functionality. They are basically versions of XGBClassifier and XGBRegressor that train random forest instead of gradient boosting, and have default values and meaning of some of the parameters adjusted accordingly." $\endgroup$
    – Oxbowerce
    Commented Feb 3, 2022 at 13:56
  • $\begingroup$ Thank you @Oxbowerce but still I am not sure what is the answer to my question Is it that XGBRFClassifier builds in parallel trees where at each tree the optimisation occurs with GB instead of information gain or gini and then aggregate these trees or it is something different? based on what you quote (that's why I actually posted the question here haha) $\endgroup$
    – Outcast
    Commented Feb 3, 2022 at 14:00

1 Answer 1


The primary difference between RandomForestClassifier and XGBRFClassifier is the package they are in. RandomForestClassifier is in the scikit-learn package and XGBRFClassifier is in the xgboost package. They are both are implementations of the Random Forest algorithm for classification.


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