I'm a J.D./Ph.D. student at Yale Law School and the UC Berkeley Department of Statistics. My research interests lie broadly at the interface of statistical machine learning, causal inference, law, and public policy. Before graduate school, I was an empirical research fellow at Harvard Law School, where I worked with the Criminal Justice Policy Program and the Access to Justice Lab. I graduated from Pomona College with a bachelor's degree in mathematics and philosophy.
Lu, B. and J. Hardin (2019+). A Unified Framework for Random Forest Prediction Error Estimation. Accepted with minor revisions by Journal of Machine Learning Research.
We introduce a unified framework for random forest prediction error estimation based on a novel estimator of the conditional prediction error distribution function. Our framework enables immediate estimation of key prediction uncertainty metrics, including conditional mean squared prediction errors, conditional biases, and conditional quantiles, by a straightforward plug-in routine. Our approach is particularly well-adapted for prediction interval estimation, which has received less attention in the random forest literature despite its practical utility; we show via simulations that our proposed prediction intervals are competitive with, and in some settings outperform, existing methods. To establish theoretical grounding for our framework, we prove pointwise uniform consistency of a more stringent version of our estimator of the conditional prediction error distribution. In addition to providing a suite of measures of prediction uncertainty, our general framework is applicable to many variants and augmentations of the random forest algorithm. The estimators introduced here are implemented in the R package forestError.
I have been a graduate student instructor for the following courses at Berkeley.
Spring 2020: Stat 158 Design and Analysis of Experiments
Spring 2019: Stat 158 Design and Analysis of Experiments