R Library for Analytics and Machine Learning
Materials for GWU DNSC 6279 and DNSC 6290.
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
A curated list of gradient boosting research papers with implementations.
NOTE: skutil is now deprecated. See its sister project: https://github.com/tgsmith61591/skoot. Original description: A set of scikit-learn and h2o extension classes (as well as caret classes for pytho...
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-Me...
Sparkling Water provides H2O functionality inside Spark cluster
Presentations from H2O meetups & conferences by the H2O.ai team
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 algor...