This article provides a non-technical guide to interpreting SHAP analyses, useful for explaining machine learning models to non-technical stakeholders, with a focus on both local and global interpretability using various visualization methods.
The article discusses techniques to improve outlier detection in tabular data by using subsets of features, known as subspaces, which can reduce the curse of dimensionality, increase interpretability, and allow for more efficient execution and tuning over time.
