v0.0.11
🚀 New UpTrain Release 🚀
In the next feature release of UpTrain, users can look forward to several exciting updates and improvements that enhance the software's functionality 🚀. This new release focuses on enhancing user experience, improving performance, and expanding the software's capabilities to better serve the needs of the machine learning community 🌟.
- Added documentation to the GitHub repository 📚: Makes it easier for users and contributors to understand and improve UpTrain by providing comprehensive documentation on the GitHub repository.
- Removed duplicated code from t-SNE and UMAP 🧹: Streamlines the codebase by eliminating redundant code, leading to a more efficient and maintainable software package.
- Logging and monitoring in background process ⏳: Enables users to log and monitor model performance and other metrics without interrupting their workflow.
- Added concept drift detection with ADWIN 📊: Enhances concept drift detection by incorporating the ADaptive WINdowing (ADWIN) algorithm for more accurate and responsive drift detection.
- Removed package dependency on UMAP and SHAP 🛠️: Simplifies the installation process and reduces potential conflicts by removing the need for the UMAP and SHAP packages.
- Better naming for edge cases 🏷️: Introduces clearer naming conventions for edge cases, making identifying and managing them easier.
- Logging Args put together 🗂️: Streamlines argument logging, making it easier to manage and track settings for your machine learning models.
- Several bug-fixes 🐛: Addresses various issues to improve the overall stability and performance of UpTrain.