Track your trees with W&B.
The wandb library includes a special callback for XGBoost. It's also easy to use the generic logging features of Weights & Biases to track large experiments, like hyperparameter sweeps.
from wandb.xgboost import wandb_callback
import xgboost as xgb
bst = xgb.train(param, train_data, num_round, watchlist,
Looking for working code examples? Check out our repository of examples on GitHub or try out a Colab notebook

Tuning your hyperparameters with Sweeps

Attaining the maximum performance out of models requires tuning hyperparameters, like tree depth and learning rate. Weights & Biases includes Sweeps, a powerful toolkit for configuring, orchestrating, and analyzing large hyperparameter testing experiments.
To learn more about these tools and see an example of how to use Sweeps with XGBoost, check out this interactive Colab notebook or try this XGBoost & Sweeps python script here
tl;dr: trees outperform linear learners on this classification dataset.