Technology
sklearn-genetic-opt
Leverage evolutionary algorithms for efficient scikit-learn hyperparameter tuning and robust feature selection.
Sklearn-genetic-opt is your high-performance alternative to standard grid and randomized search methods. The library employs evolutionary algorithms (via the `deap` package) to optimize your scikit-learn model's hyperparameters and features simultaneously. Core components include `GASearchCV` for tuning and `GAFeatureSelectionCV` for feature selection, supporting both regression and classification problems. It also provides essential utilities: custom callbacks for precise control over optimization iterations and built-in plotting functions for visualizing fitness evolution and the search space.
What builders pair with sklearn-genetic-opt
Projects using both technologies. Select a pairing to see a project.
Pairing: AutoML
Genetic Algorithms for AutoML
Pairing: Genetic Algorithms
Genetic Algorithms for AutoML
Pairing: metaheuristics
Genetic Algorithms for AutoML
Pairing: scikit-learn
Genetic Algorithms for AutoML
Recent Talks & Demos
Showing 1-1 of 1