{"library":"scikit-optimize","type":"library","category":null,"description":"Scikit-Optimize, often referred to as skopt, is a simple and efficient Python library for sequential model-based optimization. It's designed to minimize expensive and noisy black-box functions, building on top of NumPy, SciPy, and Scikit-Learn. Version 0.10.2 is the current release. The library is under active development, with releases occurring periodically, making it a robust tool for tasks like hyperparameter tuning in machine learning.","language":"python","status":"active","version":"0.10.2","tags":["bayesian optimization","hyperparameter tuning","machine learning","optimization","scikit-learn"],"install":[{"cmd":"pip install scikit-optimize","imports":["from skopt import gp_minimize","from skopt import forest_minimize","from skopt import Optimizer","from skopt import BayesSearchCV"]},{"cmd":"pip install scikit-optimize[plots]","imports":[]},{"cmd":"conda install conda-forge::scikit-optimize","imports":[]}],"homepage":"https://scikit-optimize.readthedocs.io/en/latest/contents.html","github":"https://github.com/holgern/scikit-optimize","docs":null,"changelog":null,"pypi":"https://pypi.org/project/scikit-optimize/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":50,"avg_install_s":11.7,"avg_import_s":3.33,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/scikit-optimize/compatibility"},"provenance":{"verified_status":"passing","verified_at":"Sun Jun 28","last_verified":"Sun Jun 28","next_check":"Tue Jul 28","install_tag":null}}