In prediction problems both response and covariates may have high correlation with a second group of influential regressors, that can be considered as background variables. An important challenge is to perform variable selection and importance assessment among the covariates in the presence of these variables. A clinical example is the prediction of the lean body mass (response) from bioimpedance (covariates), where anthropometric measures play the role of background variables. We introduce a reduced dataset in which the variables are defined as the residuals with respect to the background, and perform variable selection and importance assessment both in linear and random forest models. Using a clinical dataset of multi-frequency bioimpedance, we show the effectiveness of this method to select the most relevant predictors of the lean body mass beyond anthropometry.

在预测问题中，响应变量和协变量都可能与第二组有影响力的回归变量具有高度相关性，可以将其视为背景变量。一个重要的挑战是在存在这些变量的情况下在协变量之间执行变量选择和重要性评估。一个临床示例是根据生物阻抗（协变量）预测瘦体重（反应），其中人体测量指标起着背景变量的作用。我们引入了一个简化的数据集，其中将变量定义为相对于背景的残差，并在线性和随机森林模型中执行变量选择和重要性评估。使用多频生物阻抗的临床数据集，