0.2.2

[StatMixedML]

We are excited to announce the release of XGBoostLSS v0.2.2! This release brings several new features, stability improvements, and bug fixes. Here are the key highlights of this release:

New Features

• Multivariate Distributions: XGBoostLSS now supports modeling of multivariate response distributions, allowing you to capture complex dependencies among multiple target variables. We believe that the introduction of multivariate distributions in XGBoostLSS opens up new opportunities for modeling in various domains. We encourage you to explore these capabilities and share your feedback with us.
  - Benefits for Use Cases: With this feature, you can effectively model and predict joint distributions, gaining deeper insights into your data: by considering dependencies between different risk factors, such as the occurrence of multiple events or the joint severity of claims, you can improve the accuracy of risk assessments.

• Zero-Adjusted and Zero-Inflated Distribution: these new features expand the modeling capabilities and offer enhanced flexibility for various use cases.
  - A zero-adjusted distribution assumes that the zeros occur due to a separate process from the non-zero values. It models the probability of a value being zero and the probability of it being non-zero separately.
  - A zero-inflated distribution assumes that there is a single process generating the data, but with an additional component that accounts for the excess zeros. The zero-inflated distributions combine a standard distribution, such as Poisson or Negative Binomial, with a component that models the probability of excess zeros.
  - Benefits for Use Cases: The inclusion of zero-adjusted and zero-inflated distributions empowers users to handle a wide range of scenarios effectively. These distributions can be particularly valuable in areas such as insurance claims modeling, disease count prediction, anomaly detection, and many other domains with skewed or zero-inflated data. By accurately capturing the underlying data patterns, XGBoostLSS enables more precise predictions and improved decision-making.

• CRPS Score for Training (Experimental): In this release, we introduce the experimental implementation of the Continuous Ranked Probability Score (CRPS) for training of univariate distributions. CRPS is a popular probabilistic scoring metric that measures the accuracy of predicted probability distributions. It's important to note that the CRPS score for training is still in the experimental stage. While we believe it has the potential to improve model performance, further testing and evaluation are required to validate its effectiveness across different use cases.

Stability Improvements

• Model Estimation: We have also improved the stability of the model estimation process. This results in more consistent and accurate estimation of model parameters, leading to better predictions and increased model reliability.

Bug Fixes

In addition to the new features and stability improvements, we have addressed various bugs reported by the community. These bug fixes enhance the overall reliability and usability of XGBoostLSS.

General

We appreciate the valuable feedback and contributions from our users, which have helped us in making XGBoostLSS even better. We encourage you to update to this latest version to take advantage of the new features and improvements.

Thank you for your continued support, and we look forward to your feedback.

Happy modeling!

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This discussion was created from the release 0.2.2.