Multi-Modal Data Support
Handles tabular, text, and image data seamlessly, allowing users to build models across diverse data types in a unified framework.
Automatic Model Selection and Ensembling
Automatically selects the best models and combines them through ensembling and stacking to improve prediction accuracy.
Minimal Coding Required
Provides simple APIs that enable users to train high-quality models with just a few lines of code, reducing development time.
Robust Hyperparameter Optimization
Performs efficient hyperparameter tuning to optimize model performance without manual intervention.
Extensible and Open Source
Open-source under Apache 2.0 license, allowing customization and integration with other ML tools and workflows.
Support for Tabular Prediction
Specialized optimizations for tabular data, including handling missing values and categorical features automatically.