3 code implementations • 30 Jul 2018 • Rina Friedberg, Julie Tibshirani, Susan Athey, Stefan Wager
Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals, and can show poor predictive performance in the presence of strong, smooth effects.
5 code implementations • 5 Oct 2016 • Susan Athey, Julie Tibshirani, Stefan Wager
We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the solution to a set of local moment equations.
no code implementations • 21 May 2013 • Julie Tibshirani, Christopher D. Manning
Annotation errors can significantly hurt classifier performance, yet datasets are only growing noisier with the increased use of Amazon Mechanical Turk and techniques like distant supervision that automatically generate labels.