Sequential model-based optimization with a scipy.optimize interface
The main features of scikit-optimize/scikit-optimize are: Automated Machine Learning, Hyperparameter Tuning, Optimization.
Open-source alternatives to scikit-optimize/scikit-optimize include: hyperopt/hyperopt-sklearn — Hyper-parameter optimization for sklearn. hyperopt/hyperopt — Hyperopt is a Python library for hyperparameter optimization designed to minimize scalar-valued objective functions.… dragonfly/dragonfly — An open source python library for scalable Bayesian optimisation. autonomio/talos — Hyperparameter Experiments with TensorFlow and Keras. claesenm/optunity — optimization routines for hyperparameter tuning. automl/smac3 — SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization.
Hyper-parameter optimization for sklearn
Hyperopt is a Python library for hyperparameter optimization designed to minimize scalar-valued objective functions. It operates as a stochastic search space engine that finds optimal input parameters by searching through real-valued, discrete, and conditional spaces. The framework distinguishes itself through its support for complex search space configurations, allowing for conditional parameter hierarchies where specific hyperparameters are sampled only if their parent parameters meet certain criteria. It is built as an asynchronous optimization framework, decoupling the generation of searc
Hyperparameter Experiments with TensorFlow and Keras
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization