APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.

Insurance workflow wrapper for interpretML's Explainable Boosting Machine — relativities, diagnostics, monotonicity editing, GLM comparison

This project aims to analyze diabetes data using data management, captivating visualizations, and cutting-edge machine learning techniques to predict the presence of diabetes in individuals. Our robust dataset includes comprehensive health exam results and family history.