0.2.0

Enhanced Distributional Modeling with PyTorch

• XGBoostLSS 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

• XGBoostLSS 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 XGBoostLSS workflows.
• This enhancement allows for faster experimentation and easier customization.

Hyper-Parameter Optimization

• XGBoostLSS now enables the optimization of all XGBoost hyper-parameters for enhanced modeling flexibility and performance.

What's Changed:

• The syntax of XGBoostLSS 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.