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How can it be that a xgboost.cv cross-validation operation where n-folds are evaluated is quicker than a single XGBoostClassifier.fit(X,y) of the xgboost.sklearn API?

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I believe this is the answer: https://github.com/dmlc/xgboost/issues/651 the sklearn api uses n_estimators= 100 as default whereas xgb.train is using n_boost_rounds=10

As both refer to the same parameter this could explain the huge difference.

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