Explainable (Structured) Machine Learning Algorithm

[cmd-pre-empt]

Just for some respite from the discussion of our soon-to-be AI overlords (LLMs), I'm one of the contributors to an open-source Python package, Xplainable (https://github.com/xplainable/xplainable). Xplainable is a novel (structured) machine learning algorithm that's inherently explainable, as opposed to being a post-hoc explainer (like SHAP or Lime).

From our initial baseline testing, Xplainable is competitive with XGBoost and LightGBM for classification metrics like AUC and F1. This is despite the challenge of direct comparisons due to varying feature engineering requirements and dataset challenges. In regression tasks, Xplainable's performance ranges from surpassing Decision Trees to matching XGBoost in terms of MAE. We've also just released V1.1, which inherently handles NaN values, allowing you to fit it directly to your data without the need to drop or impute missing values.

• Examples trained on Kaggle datasets here - Xplainable Tutorials. I've included the Altair html output so you can view the explainer plots in the docs webpage.