OptunaAutoML

OptunaAutoML is an automatic hyperparameter optimization software framework specifically designed for machine learning. It is an open-source tool that aims to automate the search for optimal hyperparameters, making it easier and more efficient to achieve better results in model training. With OptunaAutoML, you can easily optimize hyperparameters using state-of-the-art algorithms and search strategies.

It offers a range of key features, including eager search spaces, automated search using Python conditionals, loops, and syntax, and efficient search of large spaces with pruning of unpromising trials. This framework supports a variety of machine learning and deep learning frameworks, including Chainer, TensorFlow, Keras, MXNet, Scikit-Learn, LightGBM, and XGBoost. You can seamlessly integrate OptunaAutoML into your existing workflow with these frameworks.

OptunaAutoML provides a simple and user-friendly interface, allowing you to define objective functions, suggest hyperparameters using trial objects, and create study objects to optimize the objective function. It also offers code examples, tutorials, documentation, and a supportive community to assist you in optimizing your models. Start using OptunaAutoML to automate your hyperparameter search and achieve better results in your machine learning projects