Strengths
- Open-source and free to use with permissive BSD license.
- Strong integration with scikit-learn ecosystem.
- Advanced Bayesian optimization and meta-learning techniques.
- Supports ensemble learning for improved model robustness.
- Active research community and continuous improvements.
Limitations
- Primarily focused on tabular data; limited support for unstructured data like images or text.
- Can be computationally expensive for very large datasets or complex tasks.
- Requires Python programming knowledge to use effectively.
- Documentation can be technical and challenging for beginners.