Search Results for author: Mathieu Guillame-Bert

Found 6 papers, 0 papers with code

Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library

no code implementations6 Dec 2022 Mathieu Guillame-Bert, Sebastian Bruch, Richard Stotz, Jan Pfeifer

Yggdrasil Decision Forests is a library for the training, serving and interpretation of decision forest models, targeted both at research and production work, implemented in C++, and available in C++, command line interface, Python (under the name TensorFlow Decision Forests), JavaScript, and Go.

Generative Trees: Adversarial and Copycat

no code implementations26 Jan 2022 Richard Nock, Mathieu Guillame-Bert

While Generative Adversarial Networks (GANs) achieve spectacular results on unstructured data like images, there is still a gap on tabular data, data for which state of the art supervised learning still favours to a large extent decision tree (DT)-based models.


Modeling Text with Decision Forests using Categorical-Set Splits

no code implementations21 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

Exact Distributed Training: Random Forest with Billions of Examples

no code implementations18 Apr 2018 Mathieu Guillame-Bert, Olivier Teytaud

We introduce an exact distributed algorithm to train Random Forest models as well as other decision forest models without relying on approximating best split search.

Batched Lazy Decision Trees

no code implementations8 Mar 2016 Mathieu Guillame-Bert, Artur Dubrawski

We introduce a batched lazy algorithm for supervised classification using decision trees.

General Classification

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