Accelerated and interpretable oblique random survival forests

The oblique random survival forest (RSF) is an ensemble supervised learning method for right-censored outcomes. Oblique RSF ensembles often have higher prediction accuracy than standard RSF ensembles. However, computational overhead and lack of interpretability are pervasive limitations of the oblique RSF, In this talk, I introduce a method to increase computational efficiency of the oblique RSF and a method to estimate importance of individual predictor variables with the oblique RSF. Both methods are available in the aorsf R package.

Happier version control with Git and GitHub

git and GitHub are fantastic tools for version control and collaboration. Data scientists have increasingly used GitHub as a platform for sharing their work and working together thanks to publicly available guides such as Jenny Bryan’s Happy git with R textbook. In this seminar, I walk through the basics of git and GitHub, beginning with the jargon of git and proceeding up through submitting pull requests on GitHub.