{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/4"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"}],"enrichment":{"capability":"Hyperopt performs serial and parallel hyperparameter optimization over mixed search spaces using algorithms like Tree of Parzen Estimators (TPE), Random Search, and Adaptive TPE.","skillfed_tags":["bayesian-optimization","hyperparameter-tuning","distributed-computing"],"use_cases":["Tune scikit-learn or neural network hyperparameters (learning rate, regularization, layer sizes) to minimize validation loss","Optimize conditional configurations where some parameters only apply under certain choices (e.g., different solvers with different hyperparameters)","Parallelize hyperparameter search across a Spark cluster or MongoDB-backed worker pool for large-scale experiments","Benchmark different algorithms (Random Search vs. TPE) on the same search space to understand their relative efficiency","Explore high-dimensional spaces where grid search or random search becomes prohibitively expensive"],"what_it_does":"Hyperopt is a Python library for optimizing over complex, mixed-type search spaces\u2014those containing real-valued, discrete, and conditional parameters. It implements three algorithms: Random Search, Tree of Parzen Estimators (TPE), and Adaptive TPE. The library is designed for both serial optimization on a single machine and parallel optimization across multiple workers using Apache Spark or MongoDB as backends.\n\nYou define an objective function to minimize, specify a search space using hyperopt's domain language (hp.choice, hp.uniform, hp.lognormal, etc.), and call fmin() with your chosen algorithm. Hyperopt then explores the space, returning the best parameters found. It's commonly used for tuning machine learning model hyperparameters, though it applies to any optimization problem over awkward search spaces.","worth_installing":"Yes. Hyperopt is actively maintained, has no known vulnerabilities, installs with low friction, and is well-suited for anyone doing hyperparameter optimization beyond simple grid or random search. The TPE algorithm is more sample-efficient than random search for most problems. The BSD-3-Clause license poses no restrictions. Start here if you need Bayesian optimization without the overhead of setting up a full Gaussian process library."},"id":"hyperopt","links":{"html":"https://skillfed.io/packages/hyperopt","md":"https://skillfed.io/packages/hyperopt.md","pypi":"https://pypi.org/project/hyperopt/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-24","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"hyperopt","python_support":"supports_current","summary":"Distributed Asynchronous Hyperparameter Optimization"},"popularity":{"monthly_downloads":2851699,"position":2860,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.3.0"}