DataFrame support for scikit-learn.

Nature-inspired algorithms for hyper-parameter tuning of Scikit-Learn models.

Text classification with Machine Learning and Mealpy

Hyper-parameter tuning of Time series forecasting models with 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

Using Facebook Adaptive Experimentation platform to tune random forest regressors using docker

A simple python interface for running multiple parallel instances of a python program (e.g. gridsearch).

Examples of parameter tuning via DrOpt.

CLI to create and optimize optuna study without explicit objective function