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I have highly imbalanced dataset. I am using XGBoost and I got the following results without balancing the dataset out:

Precision: 0.87
Recall: 0.79
F1: 0.83

My parameters are:

xgb_model=xgb.XGBClassifier(
    objective = "binary:logistic",
    n_estimators=500, 
    max_depth=8,
    verbosity=2,
    random_state=42)
xgb_model.fit(X_train,y_train, eval_metric='aucpr', eval_set=eval_set)

If I balance out my dataset via SMOTE, I get the following results:

Precision: 0.81
Recall: 0.80
F1: 0.81

If I use an imbalanced dataset, how do I improve recall (via precision trade-off)?

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