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.

翻译：机器学习模型预测能力的提升伴随着复杂性的增加和可解释性的丧失，尤其与参数统计模型相比更为显著。这一权衡催生了可解释人工智能（XAI），其提供局部解释（LEs）和局部变量归因（LVAs）等方法，以揭示模型如何利用预测变量得出预测结果。这些方法提供了单个观测值附近线性变量重要性的点估计。然而，LVAs通常无法有效处理预测变量之间的关联性。为理解预测变量间的交互作用对变量重要性估计的影响，我们可以将LVAs转换为线性投影，并采用径向轨迹（radial tour）进行探索。该方法还有助于了解模型如何出错、异常值的影响或观测值的聚类情况。本文通过分类响应模型（企鹅种类、巧克力类型）和定量响应模型（足球薪资、房价）的实例进行演示。相关方法已在CRAN发布的R语言包cheem中实现。