CFXplorer generates optimal distance counterfactual explanations for a given machine learning model.

Comprehensive benchmark study of feature selection techniques for predictive machine learning models on tabular data. Various feature selection methods are evaluated across different data characteristics and predictive scenarios.

Streamlined toolkit for predicting human phenotypes from UK Biobank using tree-based ensembles and linear models. Load high-dimensional SNP and covariate data, select variants via Random Feature Selection to balance accuracy and runtime, and interpret genetic plus socio-demographic feature contributions with SHAP.