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Learning Representations for Axis-Aligned Decision Forests through Input Perturbation
[article]
2020
arXiv
pre-print
Axis-aligned decision forests have long been the leading class of machine learning algorithms for modeling tabular data. In many applications of machine learning such as learning-to-rank, decision forests deliver remarkable performance. They also possess other coveted characteristics such as interpretability. Despite their widespread use and rich history, decision forests to date fail to consume raw structured data such as text, or learn effective representations for them, a factor behind the
arXiv:2007.14761v2
fatcat:ohiayzpetfelvbfocm6aquxfpq