lightgbm-org/LightGBM

Health v2 · maintenance only

Adoption not part of the score

18893 stars · 4091 forks observed · 2026-10-20

What it is AI-extracted, prompt v1, taxonomy v1, 2026-10-19, confidence 0.99

LightGBM is a high‑performance gradient boosting framework written in C++ with bindings for Python and R. It supports fast training, low memory usage, and scalable distributed and GPU learning.

Use cases

• train fast gradient‑boosted decision tree models for classification
• perform large‑scale ranking tasks with low memory footprint
• run distributed model training on a cluster
• accelerate model training using GPU support
• handle high‑dimensional tabular data for regression

When to choose

• you need a highly efficient GBDT implementation for large datasets
• you want built‑in support for parallel, distributed, or GPU training
• you require low memory consumption and fast training speed
• you are working in Python or R and need a native C++ backend

When to avoid

• your problem requires deep learning or neural networks
• you need extensive model interpretability beyond tree‑based methods
• you are constrained to pure JavaScript or browser environments
• your dataset is very small and training speed is not a concern

Facets

library · maturity active

machine-learning artificial-intelligence machine-learning data-science python cpp gradient-boosting gbdt distributed gpu decision-trees lightgbm

Member repositories

For agents

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