Real life machine learning problems exhibit distributional shifts in the data
from one time to another or from on place to another. This behavior is beyond
the scope of the traditional empirical risk minimization paradigm, which
assumes i.i.d. distribution of data over time and across locations. The
emerging field of out-of-distribution (OOD) generalization addresses this
reality with new theory and algorithms which incorporate environmental, or
era-wise information into the algorithms. So far, most research has been
focused on linear models and/or neural networks. In this research we develop
two new splitting criteria for decision trees, which allow us to apply ideas
from OOD generalization research to decision tree models, including random
forest and gradient-boosting decision trees. The new splitting criteria use
era-wise information associated with each data point to allow tree-based models
to find split points that are optimal across all disjoint eras in the data,
instead of optimal over the entire data set pooled together, which is the
default setting. We describe the new splitting criteria in detail and develop
unique experiments to showcase the benefits of these new criteria, which
improve metrics in our experiments out-of-sample. The new criteria are
incorporated into the a state-of-the-art gradient boosted decision tree model
in the Scikit-Learn code base, which is made freely available.

研究中提出了两种新的决策树分裂准则，可以将环境或逐时信息纳入基于树的模型中，以找到在数据的所有不联通时期中都是最优的分裂点，而不是整个数据集的最优设置。