Exploring Local Explanations of Nonlinear Models Using Animated Linear Projections

TL;DR

This paper introduces a visualization technique using animated linear projections to interpret local explanations of nonlinear models, enhancing understanding of predictor interactions and model behavior.

Contribution

It proposes converting local variable attributions into linear projections and applying the radial tour for improved interpretability of complex models.

Findings

Effective visualization of predictor interactions

Application to diverse categorical and quantitative data

Implementation available in R package cheem

Abstract

The increased predictive power of machine learning models comes at the cost of increased complexity and loss of interpretability, particularly in comparison to parametric statistical models. This trade-off has led to the emergence of eXplainable AI (XAI) which provides methods, such as local explanations (LEs) and local variable attributions (LVAs), to shed light on how a model use predictors to arrive at a prediction. These provide a point estimate of the linear variable importance in the vicinity of a single observation. However, LVAs tend not to effectively handle association between predictors. To understand how the interaction between predictors affects the variable importance estimate, we can convert LVAs into linear projections and use the radial tour. This is also useful for learning how a model has made a mistake, or the effect of outliers, or the clustering of observations. The approach is illustrated with examples from categorical (penguin species, chocolate types) and quantitative (soccer/football salaries, house prices) response models. The methods are implemented in the R package cheem, available on CRAN.