LightGBM

The wandb library includes a special callback for LightGBM. It's also easy to use the generic logging features of Weights & Biases to track large experiments, like hyperparameter sweeps.

from wandb.lightgbm import wandb_callback, log_summary
import lightgbm as lgb

# Log metrics to W&B
gbm = lgb.train(..., callbacks=[wandb_callback()])

# Log feature importance plot and upload model checkpoint to W&B
log_summary(gbm, save_model_checkpoint=True)

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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.

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To learn more about these tools and see an example of how to use Sweeps with XGBoost, check out this interactive Colab notebook.