Era Splitting -- Invariant Learning for Decision Trees

arXiv:2309.14496v4 Announce Type: replace
Abstract: Real-life machine learning problems exhibit distributional shifts in the data from one time to another or from one 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 …

abstract arxiv behavior beyond cs.ai cs.ce cs.lg data decision decision trees distribution life locations machine machine learning paradigm risk trees type