A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
- machine-learning
- python
- data-science
- automl
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Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
- rl
- reinforcement-learning
- stable-baselines
- openai
TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning
WARNING: Katib project is DEPRECATED, users should migrate to OptimizationJob and Kubeflow Trainer
- ai
- automl
- huggingface
- hyperparameter-tuning
Machine Learning in R
- machine-learning
- data-science
- tuning
- cran
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
- vizier
- hyperparameter-optimization
- tuning
- tuning-parameters
Open-source implementation of Google Vizier for hyper parameters tuning
- ml
- dl
- hyperparameters
- optimization
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
- bayesian-optimization
- bayesian-optimisation
- hyperparameter-optimization
- hyperparameter-tuning
Use evolutionary algorithms instead of gridsearch in scikit-learn
The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.
- pipeline
- pipeline-framework
- machine-learning
- deep-learning
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
- svm
- regression
- classification
- machine-learning