SKSurrogate

SKSurrogate is a Python toolkit that simplifies hyperparameter tuning and pipeline optimization for machine learning models. It builds upon the popular scikit-learn library, making it familiar and easy to use.

Key Features:

• Effortless Hyperparameter Tuning: Find the best settings for your machine learning models efficiently.

• AutoML Optimization: Automate the process of designing optimal pipelines for your tasks (AutoML).

• Flexible Surrogate Optimization: Leverage various surrogate models, including a custom Hilbert Space regressor, or choose from scikit-learn's wide range of regressors for optimization.

• Faster Pipeline Design: Save time finding optimized pipelines through an implemented evolutionary optimization algorithm, even if it doesn't guarantee a globally optimal solution.

Overall, SKSurrogate empowers you to streamline the optimization process for your machine learning workflows.