Learn LIGHTGBM with Real Code Examples

Use histogram-based training for speed

Enable GPU for large datasets with many features

Tune num_leaves, max_depth for balance between accuracy and overfitting

Reduce learning_rate with more boosting rounds

Use early_stopping_rounds during cross-validation

Validate and sanitize input data

Secure saved models with proper file permissions

Avoid exposing model predictions on sensitive data without anonymization

Log only anonymized feature values

Ensure proper dependency versions for reproducibility

Track training and validation metrics

Monitor overfitting and early stopping

Log feature importance and predictions

Compare multiple models and parameters

Visualize metrics with plots or dashboards

Write modular training and evaluation scripts

Document hyperparameter choices

Version control models and code

Unit test feature preprocessing

Ensure reproducibility with fixed seeds