Tree-based algorithms, especially Gradient Boosting Machines (GBM’s), are one of the most popular algorithms used. They often out-perform linear models and neural networks for tabular data since they used a boosted approach where each tree built works to fix the error of the previous tree. As the model trains, it is continuously self-correcting. H2O-3’s GBM is […]

H2O.ai today announced its participation as a launch partner for Snowflake’s Snowpark Container Services (available in private preview), which provides our joint customers with the flexibility to train, deploy, and score models all within their Snowflake account. This further expands the ease of use for data science teams to create machine learning models and deployment […]

Our new major releases of H2O are packed with new features and fixes! Some of the major highlights of these releases are the new Decision Tree algorithm, the added ability to grid over Infogram, an upgrade to the version of XGBoost and an improvement to its speed, the completion of the maximum likelihood dispersion parameter […]

Machine Learning explainability refers to understanding and interpreting the decisions and predictions made by a machine learning model. Explainability is crucial for ensuring the trustworthiness and transparency of machine learning models, particularly in high-stakes situations where the consequences of incorrect predictions can be significant. Today, several techniques are available to improve the explainability of a […]