> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-sdk-testing-latest.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
> Integrate W&B with LightGBM to log gradient boosting metrics, feature importance, and model performance automatically.
# LightGBM
export const ColabLink = ({url}) =>
Try in Colab
;
The `wandb` library includes a special callback for [LightGBM](https://lightgbm.readthedocs.io/en/latest/). It's also easy to use the generic logging features of W\&B to track large experiments, like hyperparameter sweeps.
```python theme={null}
from wandb.integration.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)
```
Looking for working code examples? Check out [our repository of examples on GitHub](https://github.com/wandb/examples/tree/master/examples/boosting-algorithms).
## Tuning your hyperparameters with Sweeps
Attaining the maximum performance out of models requires tuning hyperparameters, like tree depth and learning rate. W\&B [Sweeps](/models/sweeps/) is 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.
