Multivariate Probabilistic Regression with Natural Gradient Boosting

• URL: http://arxiv.org/abs/2106.03823v1
• Date: Mon, 7 Jun 2021 17:44:49 GMT
• Title: Multivariate Probabilistic Regression with Natural Gradient Boosting
• Authors: Michael O'Malley, Adam M. Sykulski, Rick Lumpkin, Alejandro Schuler
• Abstract summary: We propose a Natural Gradient Boosting (NGBoost) approach based on nonparametrically modeling the conditional parameters of the multivariate predictive distribution. Our method is robust, works out-of-the-box without extensive tuning, is modular with respect to the assumed target distribution, and performs competitively in comparison to existing approaches.
• Score: 63.58097881421937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many single-target regression problems require estimates of uncertainty along with the point predictions. Probabilistic regression algorithms are well-suited for these tasks. However, the options are much more limited when the prediction target is multivariate and a joint measure of uncertainty is required. For example, in predicting a 2D velocity vector a joint uncertainty would quantify the probability of any vector in the plane, which would be more expressive than two separate uncertainties on the x- and y- components. To enable joint probabilistic regression, we propose a Natural Gradient Boosting (NGBoost) approach based on nonparametrically modeling the conditional parameters of the multivariate predictive distribution. Our method is robust, works out-of-the-box without extensive tuning, is modular with respect to the assumed target distribution, and performs competitively in comparison to existing approaches. We demonstrate these claims in simulation and with a case study predicting two-dimensional oceanographic velocity data. An implementation of our method is available at https://github.com/stanfordmlgroup/ngboost.

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