hyperopt-sklearn
Hyperopt-sklearn automates hyperparameter optimization and model selection for scikit-learn machine learning pipelines.
What is hyperopt-sklearn?
Hyperopt-sklearn is a Python library that automates hyperparameter tuning and model selection for scikit-learn machine learning pipelines. It uses Hyperopt's Bayesian optimization to efficiently search through combinations of algorithms and parameters, helping data scientists build better models with less manual effort. The library supports a wide range of scikit-learn classifiers, regressors, and preprocessing components, including integrations with XGBoost and LightGBM.
Data scientists, machine learning engineers, and researchers who use scikit-learn and want to automate hyperparameter optimization and model selection to improve performance and save time.
Developers choose Hyperopt-sklearn because it seamlessly integrates with scikit-learn workflows, requires minimal code changes, and provides a comprehensive, flexible search space across numerous ML components, making advanced optimization accessible without extensive configuration.
Overview
Hyper-parameter optimization for sklearn
Use Cases
Best For
- Automating hyperparameter tuning for scikit-learn classification and regression tasks
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