Hands-On Ensemble Learning with Python

XGBoost

We will also try to classify the dataset using XGBoost. As XGBoost uses trees of a maximum depth of three, we expect that it will outperform AdaBoost without any fine-tuning. Indeed, XGBoost is able to achieve better performance in both datasets and for all metrics (as shown in the following table), compared to most previous ensembles:

Dataset	Metric	Value
Original	F1	0.846
Recall	0.787
Filtered	F1	0.849
Recall	0.809

XGBoost out-of-the-box performance

By increasing the maximum depth of each tree to five, the ensemble is able to perform even better, yielding the following results:

Dataset	Metric	Value
Original	F1	0.862
Recall	0.801
Filtered	F1	0.862
Recall ...