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Exploring local explanations of nonlinear models using animated linear projections

Author

Listed:
  • Nicholas Spyrison

    (Monash University)

  • Dianne Cook

    (Monash University)

  • Przemyslaw Biecek

    (Warsaw University of Technology)

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.

Suggested Citation

  • Nicholas Spyrison & Dianne Cook & Przemyslaw Biecek, 2025. "Exploring local explanations of nonlinear models using animated linear projections," Computational Statistics, Springer, vol. 40(2), pages 1071-1095, February.
  • Handle: RePEc:spr:compst:v:40:y:2025:i:2:d:10.1007_s00180-023-01453-2
    DOI: 10.1007/s00180-023-01453-2
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