> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/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 W\&B Python SDK (`wandb`) includes a special callback for [LightGBM](https://lightgbm.readthedocs.io/en/latest/) that automatically logs training metrics, feature importance, and model checkpoints to W\&B. You can also use the generic logging features of W\&B to track large experiments, such as hyperparameter sweeps.
Use this integration to monitor gradient boosting model performance, compare runs, and analyze feature contributions without writing custom logging code.
```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)
```
For working code examples, see the [repository of examples on GitHub](https://github.com/wandb/examples/tree/master/examples/boosting-algorithms).
## Tune your hyperparameters with Sweeps
To get the best performance from models, tune hyperparameters such as tree depth and learning rate. W\&B [Sweeps](/models/sweeps/) is a toolkit that configures, orchestrates, and analyzes large hyperparameter testing experiments.
To learn more about these tools and see an example of how to use Sweeps with XGBoost, open the following interactive Colab notebook.
