Classification trees continue to be widely adopted in machine learning
applications due to their inherently interpretable nature and scalability. We
propose a rolling subtree lookahead algorithm that combines the relative
scalability of the myopic approaches with the foresight of the optimal
approaches in constructing trees. The limited foresight embedded in our
algorithm mitigates the learning pathology observed in optimal approaches. At
the heart of our algorithm lies a novel two-depth optimal binary classification
tree formulation flexible to handle any loss function. We show that the
feasible region of this formulation is an integral polyhedron, yielding the LP
relaxation solution optimal. Through extensive computational analyses, we
demonstrate that our approach outperforms optimal and myopic approaches in 808
out of 1330 problem instances, improving the out-of-sample accuracy by up to
23.6% and 14.4%, respectively.

提出一种基于滚动子树向前算法的分类树构建算法，能够有效解决最优算法存在的学习病理问题，并在大量计算分析实验中证明其相较于最优算法与局部最优算法能够提升 23.6% 和 14.4% 的模型准确率

滚动前瞻学习最优分类树

Rolling Lookahead Learning for Optimal Classification Trees

Post-editing (PE) machine translation (MT) is widely used for dissemination
because it leads to higher productivity than human translation from scratch
(HT). In addition, PE translations are found to be of equal or better quality
than HTs. However, most such studies measure quality solely as the number of
errors. We conduct a set of computational analyses in which we compare PE
against HT on three different datasets that cover five translation directions
with measures that address different translation universals and laws of
translation: simplification, normalisation and interference. We find out that
PEs are simpler and more normalised and have a higher degree of interference
from the source language than HTs.

机器翻译后编辑广泛应用于传播，其高生产率和质量优于从零开始的人工翻译。本研究通过计算分析比较后编辑和人工翻译在五个翻译方向上的不同翻译普遍规律，发现后编辑更简化、规则化，但受源语言干扰程度更高。