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增强是以增加复杂性和失去可解释性为代价的，尤其与参数统计模型相比。这种权衡催生了可解释人工智能（XAI），它提供了诸如局部解释（LEs）和局部变量归因（LVAs）等方法，用以阐明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点附近线性变量重要性的点估计。然而，LVAs往往无法有效处理预测变量之间的关联。为了理解预测变量间的交互作用如何影响变量重要性估计，我们可以将LVAs转换为线性投影，并使用径向漫游。这也有助于了解模型如何产生错误、异常值的影响或观测值的聚类情况。该方法通过分类（企鹅物种、巧克力类型）和量化（足球/英式足球薪资、房价）响应模型示例进行了说明。相关方法已在CRAN上提供的R包cheem中实现。