H2o Libraries

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H2o Libraries

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.

GitHub GitHub issues -- file bug reports / track issues here Stack Overflow -- ask all code/software questions here Cross Validated (Stack Exchange) -- ask algorithm/theory questions here h2ostream G…

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

The algorithms studied are in various commonly used open source implementations like In summary, we are focusing on which algos/implementations can be used to train relatively accurate binary classif…

Tutorials and training material for the H2O Machine Learning Platform

This document contains tutorials and training materials for H2O-3. If you find any problems with the tutorial code, please open an issue in this repository. There are a number of tutorials on all so…

This project contains examples which demonstrate how to deploy analytic models to mission-critical, scalable production environments leveraging Apache Kafka and its Streams API. Models are built with Python, H2O, TensorFlow, Keras, DeepLearning4 and other technologies.

Here is some material about this topic if you want to read and listen to the theory instead of just doing hands-on: Here some more demos: The code is developed and tested on Mac and Linux operating s…

interpretable_machine_learning_with_python

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

Examples of techniques for training interpretable machine learning (ML) models, explaining ML models, and debugging ML models for accuracy, discrimination, and security. The notebooks highlight techn…

H2O.ai Machine Learning Interpretability Resources

Machine learning algorithms create potentially more accurate models than linear models, but any increase in accuracy over more traditional, better-understood, and more easily explainable techniques i…

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