Library/h2o-3Forked

h2oai/h2o-3

h2o-3

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

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Builder

h2oai

h2oai • individual

Stars

7,523

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Forks

2,034

Using upstream fork count

Open Issues

Activity Score

0/100

1 commits in 30d

Created

Mar 3, 2014

Project creation date

README Summary

H2O-3 is an open source, distributed machine learning platform that provides fast and scalable implementations of popular algorithms including deep learning, gradient boosting, random forest, and automated machine learning (AutoML). The platform is designed for big data processing and offers APIs in multiple languages including R, Python, Scala, and Java. It features both supervised and unsupervised learning capabilities with enterprise-grade performance and scalability.

AI Dev Skills

Unmapped

Tags

Distributed Machine LearningGradient Boosting MachinesDeep Learning Neural NetworksRandom Forest EnsemblesGeneralized Linear ModelingElastic Net RegularizationK-Means ClusteringPrincipal Component AnalysisGeneralized Additive ModelsRule-based LearningSupport Vector MachinesStacked Ensemble MethodsAutomated Machine LearningXGBoost ImplementationHyperparameter OptimizationFeature EngineeringModel InterpretabilityEnsemble LearningDistributed Data ProcessingCross-validation and Model SelectionDistributed ClustersAutomated Machine Learning PipelinesFeature Selection and EngineeringEnsemble Model DevelopmentOn-premiseCloudLarge-scale Predictive ModelingHyperparameter Tuning at ScaleExplainable AIModel Performance BenchmarkingSelf-hostedDistributed AI SystemsTabularJupyter Notebook

Taxonomy

AI Trends

Automated Machine Learning Distributed AI Systems Ensemble Learning Explainable AI

Deployment Context

Cloud On-premise Distributed Clusters Self-hosted

Modalities

Tabular

Skill Areas

Use Cases

Automated Machine Learning Pipelines Large-scale Predictive Modeling Ensemble Model Development Distributed Data Processing Feature Selection and Engineering Model Performance Benchmarking Hyperparameter Tuning at Scale Cross-validation and Model Selection

Recent Activity

Updated 22 days ago

7 Days

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90 Days

Quality

production

Quality: high
Maturity: production

PM Skills

Scale & ReliabilityDeveloper Platform

Languages

Jupyter Notebook100.0%

Timeline

Project created: Mar 3, 2014
Forked: Mar 22, 2026
Your last push: 22 days ago
Upstream last push: 7 days ago
Tracked since: Mar 22, 2026

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