Track your trees with W&B.
from wandb.xgboost import wandb_callback
import xgboost as xgb
bst = xgb.train(param, train_data, num_round, watchlist,
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.
tl;dr: trees outperform linear learners on this classification dataset.