DataFrame support for scikit-learn.
Nature-inspired algorithms for hyper-parameter tuning of Scikit-Learn models.
Hyper-parameter tuning of Time series forecasting models with Mealpy
Text classification with Machine Learning and Mealpy
Combined hyper-parameter optimization and feature selection for machine learning models using micro genetic algorithms
Hyper-parameter tuning of classification model with Mealpy
A gradient free optimization routine which combines Particle Swarm Optimization with a local optimization for each particle
Surrogate adaptive randomized search for hyper-parameters tuning in sklearn.
Efficient and Scalable Batch Bayesian Optimization Using K-Means
A simple python interface for running multiple parallel instances of a python program (e.g. gridsearch).
Using Facebook Adaptive Experimentation platform to tune random forest regressors using docker
Examples of parameter tuning via DrOpt.
CLI to create and optimize optuna study without explicit objective function
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