The Best Scikit-Opt Alternatives in 2026

eaopt provides Go libraries for genetic algorithms, particle swarm optimization, and differential evolution.

Opytimizer provides Python implementations of metaheuristic optimization algorithms for search problems.

BayesO provides a Python package for Bayesian optimization algorithms and related modeling tasks.

ML-Ensemble provides Python tools for high-performance ensemble learning with scikit-learn.

This Scikit-learn extension accelerates compatible applications through optimized implementations.

TensorFlow Model Optimization provides Keras and TensorFlow tools for model quantization and pruning.

Karate Club offers algorithms for unsupervised learning on graph data in Python.

This metric-learn repository contains Python implementations of metric learning algorithms.

metric-learn provides Python implementations of metric learning algorithms.

numpy-ml collects machine learning algorithms in NumPy, presented as an intentionally readable Python package.

pomegranate provides flexible probabilistic modeling tools and algorithms for Python users.

hyperopt-sklearn uses hyperparameter optimization to select machine learning algorithms for scikit-learn.

PyTorch Lightning extension that accelerates and enhances foundation model experimentation with flexible fine-tuning schedules.

Deeplearning4j provides tools for training and deploying deep learning models in the JVM.

autoai automates regression and classification modeling in Python through model search and hyperparameter tuning.

Katib automates machine learning on Kubernetes, including hyperparameter tuning and neural architecture search.

KerasTuner searches hyperparameters to optimize machine learning models built with Keras.

OpenMetricLearning provides metric learning pipelines, models, and a model zoo.

Hugging Face Accelerate launches and trains PyTorch models across devices and distributed configurations.

Learnergy is a Python library for energy-based machine learning models.

Octo provides a transformer-based policy for robots, with code to train and fine-tune it.

FEDOT automates machine learning model development through its modeling framework.

Enables single machine or distributed training and evaluation of deep learning models.

LightlyTrain supports pretraining, fine-tuning, and distillation for computer vision models.