sklearn-deap
Optimization framework
Replaces grid search with evolutionary algorithms to find optimal parameters for machine learning models
Use evolutionary algorithms instead of gridsearch in scikit-learn
771 stars
30 watching
130 forks
Language: Jupyter Notebook
last commit: about 1 year ago
Linked from 3 awesome lists
Related projects:
Repository | Description | Stars |
---|---|---|
| Automated hyperparameter tuning and feature selection using evolutionary algorithms. | 316 |
| Automates search for optimal parameters in machine learning algorithms. | 1,594 |
| An evolutionary optimization library that provides multiple algorithms and interfaces to solve complex optimization problems using genetic and other optimization techniques. | 890 |
| Automates feature engineering by using genetic programming to select the most useful features for machine learning models. | 51 |
| An evolutionary algorithm for designing neural networks in Keras | 951 |
| A framework for distributed optimization with communication compression and optimal oracle complexity. | 0 |
| A comprehensive Java library of local search algorithms with customization and hybridization capabilities | 60 |
| Improves the performance of deep neural networks by selectively stopping training at different stages | 29 |
| A tool for exploring and optimizing the architecture of Convolutional Neural Networks using a Genetic Algorithm | 218 |
| A comprehensive framework for practical machine learning in Ruby. | 20 |
| A framework for executing genetic algorithms in Rust | 75 |
| Improves the performance of Generative Adversarial Networks by normalizing weights and batch data | 181 |
| A modular JAX implementation of federated learning via posterior averaging for decentralized optimization | 50 |
| A collection of stochastic optimization algorithms for large-scale machine learning problems | 221 |
| Automates the search for optimal neural network configurations in deep learning applications | 468 |