Automated Model Training
Automatically trains and tunes multiple ML algorithms to find the best performing model.
Stacked Ensembles
Combines multiple models to improve prediction accuracy through ensemble learning.
Open-Source and Extensible
Fully open-source with APIs for Python, R, and REST, enabling customization and integration.
Distributed Computing
Runs on clusters to scale model training for big data workloads efficiently.
Model Interpretability
Includes tools for explainability such as variable importance and partial dependence plots.
Easy Deployment
Supports exporting models for deployment in production environments via MOJO or POJO formats.