LightGBM

Gradient boosting that trains fast on big tables
ML frameworks & MLOpsGradient boostingMITRuns locallyC++Beginner
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What is LightGBM?

LightGBM trains faster and uses less memory than XGBoost on large datasets, with comparable accuracy.

Why people choose LightGBM

LightGBM at a glance

CategoryML frameworks & MLOps
TypeGradient boosting
LicenseMIT
Runs locallyYes
Built withC++
Skill levelBeginner
Best forlarge tabular datasets where training time is the bottleneck

Open-source alternatives to LightGBM

Other open-source ml frameworks & mlops tools worth comparing:

DagsterOrchestration that thinks in data assets, not tasksTensorFlowGoogle's deep-learning framework, built for productionPyTorchThe framework nearly every modern AI model is written inOpenCVThe computer vision library everything else builds onscikit-learnClassical machine learning, done properlyApache AirflowSchedule and monitor data pipelinesRayScale Python from a laptop to a clusterJAXNumPy with autodiff, JIT and TPUsXGBoostStill the one to beat on tabular dataLabel StudioLabel anything — text, images, audio, videoMLflowTrack experiments and ship models without the spreadsheetONNXMove a model between frameworks and runtimesCVATSerious annotation for computer visionDVCGit for datasets and modelsOptunaFind the right hyperparameters without guessing

LightGBM head-to-head

LightGBM vs DagsterLightGBM vs TensorFlowLightGBM vs PyTorchLightGBM vs OpenCVLightGBM vs scikit-learnLightGBM vs Apache AirflowLightGBM vs RayLightGBM vs JAXLightGBM vs XGBoostLightGBM vs Label StudioLightGBM vs MLflowLightGBM vs ONNXLightGBM vs CVATLightGBM vs DVCLightGBM vs Optuna

FAQ

Is LightGBM free?

LightGBM is free and open-source (MIT license), so you can use, self-host and modify it at no cost.

Can I run LightGBM locally?

Yes. LightGBM is designed to run on your own machine or server, keeping your data private.

What is the best alternative to LightGBM?

Popular open-source alternatives include Dagster, TensorFlow, PyTorch. See the comparisons above to choose.

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