lightgbm-org/LightGBM

Health v2 · maintenance only

Adoption not part of the score

18714 stars · 4057 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

LightGBM is a fast, distributed, high-performance gradient boosting framework based on decision tree algorithms, with APIs for Python, R, C, and other languages. It supports parallel, distributed, and GPU learning and is designed for large-scale data with low memory usage.

Use cases

• train gradient boosted decision tree models for classification
• build ranking models for search and recommendation
• fit models on large datasets with distributed training
• speed up model training with GPU learning
• compete in tabular machine learning competitions like Kaggle
• tune GBDT hyperparameters for better accuracy

When to choose

• you need fast, memory-efficient gradient boosting on tabular data
• your dataset is too large for a single machine and you need distributed training
• you want GPU-accelerated tree learning
• you need mature Python, R, or C APIs for boosting

When to avoid

• you need deep learning on unstructured data like images or text
• you want a simple, interpretable linear model
• your project requires a different ecosystem like Spark MLlib exclusively

Facets

library · maturity stable

machine-learning data-science gpu-computing machine-learning data-science big-data python cpp cross-platform windows gradient-boosting gbdt decision-trees distributed-training ranking classification regression kaggle gpu linux macos

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markdown · JSON · MCP: product_card(name="lightgbm-org/LightGBM")