Modeling Text with Decision Forests using Categorical-Set Splits

no code implementations • 21 Sep 2020 • Mathieu Guillame-Bert, Sebastian Bruch, Petr Mitrichev, Petr Mikheev, Jan Pfeifer

We define a condition that is specific to categorical-set features -- defined as an unordered set of categorical variables -- and present an algorithm to learn it, thereby equipping decision forests with the ability to directly model text, albeit without preserving sequential order.

text-classification Text Classification

Interpretable Learning-to-Rank with Generalized Additive Models

no code implementations • 6 May 2020 • Honglei Zhuang, Xuanhui Wang, Michael Bendersky, Alexander Grushetsky, Yonghui Wu, Petr Mitrichev, Ethan Sterling, Nathan Bell, Walker Ravina, Hai Qian

Interpretability of learning-to-rank models is a crucial yet relatively under-examined research area.

Additive models Interpretable Machine Learning +2

TF Boosted Trees: A scalable TensorFlow based framework for gradient boosting

no code implementations • 31 Oct 2017 • Natalia Ponomareva, Soroush Radpour, Gilbert Hendry, Salem Haykal, Thomas Colthurst, Petr Mitrichev, Alexander Grushetsky

TF Boosted Trees (TFBT) is a new open-sourced frame-work for the distributed training of gradient boosted trees.