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scikit-opt is a Python optimization library and numerical framework designed to solve complex global optimization problems. It provides a suite of metaheuristic algorithms and tools for finding global minima or maxima of objective functions. The library implements a variety of nature-inspired and swarm intelligence algorithms, including Genetic Algorithms, Particle Swarm Optimization, Differential Evolution, Simulated Annealing, and Ant Colony Optimization. It includes specialized solvers for discrete combinatorial challenges, such as the Traveling Salesman Problem. The framework supports th
Hyperparameter Experiments with TensorFlow and Keras
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
Python package for Bayesian Machine Learning with scikit-learn API
Use evolutionary algorithms instead of gridsearch in scikit-learn
The main features of rsteca/sklearn-deap are: General Machine Learning, Hyperparameter Tuning, Machine Learning Frameworks, Machine Learning Packages, Optimization.
Open-source alternatives to rsteca/sklearn-deap include: guofei9987/scikit-opt — scikit-opt is a Python optimization library and numerical framework designed to solve complex global optimization… csinva/imodels — Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible). aksnzhy/xlearn — High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization… amazaspshumik/sklearn-bayes — Python package for Bayesian Machine Learning with scikit-learn API. christophm/rulefit — Python implementation of the rulefit algorithm. autonomio/talos — Hyperparameter Experiments with TensorFlow and Keras.