auto-sklearn vs EvalML
A side-by-side comparison of two AutoML AI agents — to help you pick the right one.
auto-sklearn
auto-sklearn is an automated machine learning tool that extends scikit-learn by automatically searching for the best machine learning pipeline, including model selection and hyperparameter tuning. It leverages meta-learning and Bayesian optimization to improve efficiency and performance.
EvalML
EvalML is an open-source AutoML library that automates the process of building, optimizing, and evaluating machine learning pipelines. It specializes in using domain-specific objective functions to tailor models for specific business or research needs. The tool integrates with scikit-learn and provides a streamlined workflow for model selection and hyperparameter tuning.
| auto-sklearn | EvalML | |
|---|---|---|
| Category | AutoML | AutoML |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Skill level | Intermediate | Intermediate |
| Pricing | Open Source | Open Source |
auto-sklearn: what it solves
It eliminates the need for manual trial-and-error in selecting and tuning machine learning models, saving time and improving model accuracy.
EvalML: what it solves
It eliminates the manual effort of trial-and-error in selecting and tuning machine learning models, particularly for domain-specific tasks where standard metrics may not suffice.