2020/07/11 by Alvaro H. C. Correia, Correia, Alvaro H. C., Robert Peharz +4 · 1 citation
Computer Science · Mathematics · #Anomaly Detection Techniques and Applications #Generative Adversarial Networks and Image Synthesis #Machine Learning and Data Classification #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2007.05721
Presented at the ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning
arxiv created 2020/07/11 · arxiv updated 2020/07/14
Decision Trees and Random Forests are among the most widely used machine learning models, and often achieve state-of-the-art performance in tabular, domain-agnostic datasets. Nonetheless, being primarily discriminative models they lack principled methods to manipulate the uncertainty of predictions. In this paper, we exploit Generative Forests (GeFs), a recent class of deep probabilistic models that addresses these issues by extending Random Forests to generative models representing the full joint distribution over the feature space. We demonstrate that GeFs are uncertainty-aware classifiers, capable of measuring the robustness of each prediction as well as detecting out-of-distribution samples.