Analytics & Machine Learning R Sidekick
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Updated
Jul 16, 2024 - R
Analytics & Machine Learning R Sidekick
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.
Presentación, código y ejemplos del Machine Learning Spain XXVII
Production ready templates for deploying Driverless AI (DAI) scorers. https://h2oai.github.io/dai-deployment-templates/
Sparkling Water provides H2O functionality inside Spark cluster
Comparison tools
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
Prediction of Academic success using the automated machine learning tool : H2O AutoML
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:
Tutorials and training material for the H2O Machine Learning Platform
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.
A curated list of gradient boosting research papers with implementations.
ForestFlow is a policy-driven Machine Learning Model Server. It is an LF AI Foundation incubation project.
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