Learn LIGHTGBM with Real Code Examples

LightGBM enables efficient training of large-scale datasets with lower memory usage.

It implements gradient-based one-side sampling (GOSS) and exclusive feature bundling (EFB) for speed and accuracy.

LightGBM integrates seamlessly with Python ML workflows, including scikit-learn, XGBoost, and other pipelines.

Gradient-based One-Side Sampling (GOSS)

Exclusive Feature Bundling (EFB)

Leaf-wise tree growth with depth limitation

Support for custom objective functions

Integration with Python, R, and CLI interfaces

Dataset: structured tabular data with features and labels

Booster: core model object (tree-based)

Leaf-wise tree growth: splits the most important leaf

Objective function: defines learning goal (e.g., regression, classification)

Hyperparameters: control learning rate, depth, boosting type, etc.

main.py / notebook.ipynb - training and evaluation scripts

data/ - raw and preprocessed datasets

models/ - saved LightGBM model files

utils/ - feature engineering and helper functions

notebooks/ - experiments and parameter tuning

Prepare data: train/test split, categorical encoding

Create Dataset objects for LightGBM

Define parameters for training

Train using lgb.train or LGBMClassifier/LGBMRegressor

Evaluate performance and tune hyperparameters

Beginner: train basic classification/regression models

Intermediate: hyperparameter tuning, cross-validation

Advanced: ranking, custom objectives, GPU training

Expert: distributed learning, large-scale optimization

Enterprise: production deployment and monitoring

LightGBM vs XGBoost: faster and more memory-efficient

LightGBM vs CatBoost: better for categorical-heavy data

LightGBM vs RandomForest: gradient boosting vs bagging

LightGBM vs scikit-learn GBM: highly optimized for large datasets

LightGBM vs TensorFlow/PyTorch: tabular ML vs deep learning

2016 – LightGBM released by Microsoft DMTK team

2017 – Improved GOSS and EFB features

2018 – Added GPU training support

2019 – Enhanced categorical feature handling

2025 – LightGBM 4.x with distributed training improvements

Leaf-wise tree growth: splits leaf with max delta loss

GOSS: Gradient-based One-Side Sampling

EFB: Exclusive Feature Bundling

Booster: model object

Objective function: learning target (regression/classification)