3.24.0.4
  • Welcome to H2O 3
  • Quick Start Videos
  • Cloud Integration
  • Downloading & Installing H2O
  • Starting H2O
  • Getting Data into Your H2O Cluster
  • Data Manipulation
  • Algorithms
    • Common
      • Quantiles
      • Early Stopping
    • Supervised
      • Cox Proportional Hazards (CoxPH)
      • Deep Learning (Neural Networks)
      • Distributed Random Forest (DRF)
      • Generalized Linear Model (GLM)
      • Gradient Boosting Machine (GBM)
      • Naïve Bayes Classifier
      • Stacked Ensembles
      • XGBoost
    • Unsupervised
      • Aggregator
      • Generalized Low Rank Models (GLRM)
      • Isolation Forest
      • K-Means Clustering
      • Principal Component Analysis (PCA)
    • Miscellaneous
      • Word2vec
    • MOJO Import
      • Mojo Import
  • Cross-Validation
  • Variable Importance
  • Grid (Hyperparameter) Search
  • Checkpointing Models
  • Performance and Prediction
  • AutoML: Automatic Machine Learning
  • Saving and Loading a Model
  • Productionizing H2O
  • Using Flow - H2O’s Web UI
  • Downloading Logs
  • H2O Architecture
  • Security
  • FAQ
  • Glossary
  • Migration Guide
  • Appendix A - Parameters
  • Appendix B - API Reference
H2O
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  • Algorithms
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Algorithms¶

This section provides an overview of each algorithm available in H2O. For detailed information about the parameters that can be used for building models, refer to Appendix A - Parameters.

Common¶

  • Quantiles
  • Early Stopping

Supervised¶

  • Cox Proportional Hazards (CoxPH)
  • Deep Learning (Neural Networks)
  • Distributed Random Forest (DRF)
  • Generalized Linear Model (GLM)
  • Gradient Boosting Machine (GBM)
  • Naïve Bayes Classifier
  • Stacked Ensembles
  • XGBoost

Unsupervised¶

  • Aggregator
  • Generalized Low Rank Models (GLRM)
  • Isolation Forest
  • K-Means Clustering
  • Principal Component Analysis (PCA)

Miscellaneous¶

  • Word2vec

MOJO Import¶

  • Mojo Import
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