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.

Analytics & Machine Learning R Sidekick

Prediction of Academic success using the automated machine learning tool : H2O AutoML

Este repositorio contiene un ejemplo de como usar H2O con MariaDB.

Sparkling Water provides H2O functionality inside Spark cluster

R package for automation of machine learning, forecasting, model evaluation, and model interpretation

This project focuses on analyzing diabetes-related data and building predictive models using KNIME. The main objectives of the project include:

Add oauth2 authentication layer with ngx_http_auth_request_module

Tutorials and training material for the H2O Machine Learning Platform

Comparison tools

This project is a classification problem with a response variable to classify handwritten images as the numbers, 'one', 'seven' or 'eight. This is a classification problem using the Cross Entropy loss function, the 'tanh' activation function using h2o deeplearning as well as Trees.

autoEnsemble : An AutoML Algorithm for Building Homogeneous and Heterogeneous Stacked Ensemble Models by Searching for Diverse Base-Learners

A Python Data Project using H2O and Decision Trees to predict the 2024 Premier League Table based on the previous 30 years.

Ultra-high-resolution water and steam property calculation, based on IAPWS-IF 97

A curated list of gradient boosting research papers with implementations.

Production ready templates for deploying Driverless AI (DAI) scorers. https://h2oai.github.io/dai-deployment-templates/

DayF (Decision at your Fingertips) is an AutoML opensource project

Forecasting with H2O AutoML. Use the H2O Automatic Machine Learning algorithm as a backend for Modeltime Time Series Forecasting.

ForestFlow is a policy-driven Machine Learning Model Server. It is an LF AI Foundation incubation project.