{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/6"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"}],"enrichment":{"capability":"Scikit-Optimize is a library for optimizing expensive, noisy black-box functions using sequential model-based optimization methods, without requiring gradients.","skillfed_tags":["optimization","hyperparameter-tuning","bayesian-methods"],"use_cases":["Tune machine learning model hyperparameters when grid or random search is too slow","Optimize expensive simulation or experimental parameters with noisy outcomes","Find optimal configurations for systems where each evaluation takes significant time or resources","Explore high-dimensional parameter spaces without gradient information","Visualize and understand objective function behavior across parameter ranges"],"what_it_does":"Scikit-Optimize implements sequential model-based optimization for functions that are expensive to evaluate, noisy, or lack gradient information. It builds on numpy, scipy, and scikit-learn to provide methods like Gaussian process minimization that iteratively suggest promising parameter values, evaluate them, and refine the model. The library is designed for hyperparameter tuning, experimental design, and other settings where you need to find good inputs without access to derivatives or with high evaluation cost.\n\nThe package offers both high-level functions for simple use cases and lower-level Optimizer classes for fine-grained control over the optimization loop. It includes plotting utilities for visualizing the objective function landscape and optimization progress, available via an optional extra that adds matplotlib.","worth_installing":"Yes, with caution on maintenance. Scikit-Optimize is a solid, permissively licensed tool for black-box optimization with low install friction and no known vulnerabilities. However, the last release was 801 days ago and maintenance is dormant. If your use case is stable and you do not need recent dependency updates or bug fixes, it is safe to use; if you need active support or compatibility with the latest Python or dependency versions, verify that the fork's current state meets your needs before committing."},"id":"scikit-optimize","links":{"html":"https://skillfed.io/packages/scikit-optimize","md":"https://skillfed.io/packages/scikit-optimize.md","pypi":"https://pypi.org/project/scikit-optimize/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-06-04","license_spdx":null,"license_treatment":"permissive","name":"scikit-optimize","python_support":"unspecified","summary":"Sequential model-based optimization toolbox."},"popularity":{"monthly_downloads":810537,"position":5008,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.10.2"}