Enhanced Distributional Modeling with PyTorch
- LightGBMLSS now fully relies on PyTorch distributions for distributional modeling.
- The integration with PyTorch distributions provides a more comprehensive and flexible framework for probabilistic modeling and uncertainty estimation.
- Users can leverage the rich set of distributional families and associated functions offered by PyTorch, allowing for a wider range of modeling options.
Automatic Differentiation
- LightGBMLSS now fully leverages PyTorch's automatic differentiation capabilities.
- Automatic differentiation enables efficient and accurate computation of gradients and hessians, resulting in enhanced model performance and flexibility.
- Users can take advantage of automatic differentiation to easily incorporate custom loss functions into their LightGBMLSS workflows.
- This enhancement allows for faster experimentation and easier customization.
Hyper-Parameter Optimization
- LightGBMLSS now enables the optimization of all LightGBM hyper-parameters for enhanced modeling flexibility and performance.
What's Changed:
- The syntax of LightGBMLSS has been updated in this release. We have made improvements to certain aspects of the syntax to provide better clarity and consistency.
- To familiarize yourself with the updated syntax, we kindly refer you to the example sections. The examples will demonstrate the revised syntax and help you adapt your code accordingly.
Bug Fixes
- Several minor fixes and improvements have been implemented in this release.