Accelerated and interpretable oblique random survival forests
By Byron C Jaeger in Talk
June 21, 2022
Abstract
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.
Date
June 21, 2022
Time
12:00 AM
Location
Gainesville, FL
Event
I have given this talk in multiple places, but its original venue was the International Chinese Statistical Association’s Applied Statistics Symposium. I am also continuing to develop the slides in between talks.