Search Results for author:
Found 6 papers, 0 papers with code
Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library
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
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
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.
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation
Axis-aligned decision forests have long been the leading class of machine learning algorithms for modeling tabular data.
Exact Distributed Training: Random Forest with Billions of Examples
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
We introduce a batched lazy algorithm for supervised classification using decision trees.
