{"repo":"stanfordmlgroup/ngboost","free":true,"listed":false,"github":"https://github.com/stanfordmlgroup/ngboost","clone":"git clone https://github.com/stanfordmlgroup/ngboost.git","description":"Natural Gradient Boosting for Probabilistic Prediction","language":"Jupyter Notebook","stars":1888,"topics":["machine-learning","gradient-boosting","natural-gradients","uncertainty-estimation","ngboost","python"],"license":"Apache-2.0","category":"machine-learning","readme_excerpt":"NGBoost: Natural Gradient Boosting for Probabilistic Prediction ngboost is a Python library that implements Natural Gradient Boosting, as described in \"NGBoost: Natural Gradient Boosting for Probabilistic Prediction\". It is built on top of Scikit-Learn, and is designed to be scalable and modular with respect to choice of proper scoring rule, distribution, and base learner. A didactic introduction to the methodology underlying NGBoost is available in this slide deck. Installation Usage Probabilistic regression example on the Boston housing dataset: Details on available distributions, scoring rules, learners, tuning, and model interpretation are available in our user guide, which also includes numerous usage examples and information on how to add new distributions or scores to NGBoost. License Apache License 2.0. Reference Tony Duan, Anand Avati, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler. 2019. NGBoost: Natural Gradient Boosting for Probabilistic Prediction. arXiv","default_branch":null,"files":null,"tree":[],"storefront":"/r/stanfordmlgroup","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/stanfordmlgroup/ngboost/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}