A comparative analysis of machine learning models in SHAP analysis

TL;DR

This paper compares how different machine learning models' SHAP analysis interpretations vary across data sets, introduces a generalized waterfall plot for multi-classification, and aims to guide analysts in explainable AI.

Contribution

It provides a detailed comparison of SHAP analysis across models and data sets, and proposes a new visualization method for multi-classification explanations.

Findings

SHAP interpretation varies significantly between models.

The generalized waterfall plot enhances multi-classification explanation.

Insights help improve trustworthiness of black-box models.

Abstract

In this growing age of data and technology, large black-box models are becoming the norm due to their ability to handle vast amounts of data and learn incredibly complex data patterns. The deficiency of these methods, however, is their inability to explain the prediction process, making them untrustworthy and their use precarious in high-stakes situations. SHapley Additive exPlanations (SHAP) analysis is an explainable AI method growing in popularity for its ability to explain model predictions in terms of the original features. For each sample and feature in the data set, an associated SHAP value quantifies the contribution of that feature to the prediction of that sample. Analysis of these SHAP values provides valuable insight into the model's decision-making process, which can be leveraged to create data-driven solutions. The interpretation of these SHAP values, however, is model-dependent, so there does not exist a universal analysis procedure. To aid in these efforts, we present a detailed investigation of SHAP analysis across various machine learning models and data sets. In uncovering the details and nuance behind SHAP analysis, we hope to empower analysts in this less-explored territory. We also present a novel generalization of the waterfall plot to the multi-classification problem.