Interaction forests: Identifying and exploiting interpretable quantitative and qualitative interaction effects
提出交互森林方法，通过双变量分裂显式建模定量和定性交互效应，并引入效应重要性度量（EIM）对协变量对排序，在220个数据集上预测优于传统随机森林，适用于需要可解释交互效应的预测建模。
Although interaction effects can be exploited to improve predictions and allow for valuable insights into covariate interplay, they are given limited attention in analysis. Interaction forests are a variant of random forests for categorical, continuous, and survival outcomes that explicitly models quantitative and qualitative interaction effects in bivariable splits performed by the trees constituting the forests. The new effect importance measure (EIM) associated with interaction forests allows for ranking of covariate pairs with respect to their interaction effects' importance to prediction. Using EIM, separate importance value lists for univariable effects, quantitative interaction effects, and qualitative interaction effects are obtained. In the spirit of interpretable machine learning, the bivariable split types of interaction forests target easily interpretable and communicable interaction effects. To learn about the nature of the interplay between covariates identified as interacting it is convenient to visualise their estimated bivariable influence. Functions that perform this task are provided in the R package diversityForest, which implements interaction forests. In a large-scale empirical study using 220 data sets, interaction forests tended to deliver better predictions than conventional random forests and competing random forest variants that use multivariable splitting. In a simulation study, EIM delivered considerably better rankings for the relevant quantitative and qualitative interaction effects than competing approaches. These results indicate that interaction forests are suitable tools for the challenging task of identifying and making use of easily interpretable and communicable interaction effects in predictive modelling.